Python2/PyMuPDF: mupdf-source/thirdparty/leptonica/src/recogtrain.c comparison

comparison mupdf-source/thirdparty/leptonica/src/recogtrain.c @ 2:b50eed0cc0ef upstream

ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4. The directory name has changed: no version number in the expanded directory now.

author	Franz Glasner <fzglas.hg@dom66.de>
date	Mon, 15 Sep 2025 11:43:07 +0200
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-:1d09e1dec1d9
+:b50eed0cc0ef
+/*====================================================================*
+-  Copyright (C) 2001 Leptonica.  All rights reserved.
+-
+-  Redistribution and use in source and binary forms, with or without
+-  modification, are permitted provided that the following conditions
+-  are met:
+-  1. Redistributions of source code must retain the above copyright
+-     notice, this list of conditions and the following disclaimer.
+-  2. Redistributions in binary form must reproduce the above
+-     copyright notice, this list of conditions and the following
+-     disclaimer in the documentation and/or other materials
+-     provided with the distribution.
+-
+-  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+-  ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+-  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+-  A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL ANY
+-  CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+-  EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+-  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+-  PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+-  OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+-  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+-  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*====================================================================*/
+/*!
+* \file recogtrain.c
+* <pre>
+*
+*      Training on labeled data
+*         l_int32             recogTrainLabeled()
+*         PIX                *recogProcessLabeled()
+*         l_int32             recogAddSample()
+*         PIX                *recogModifyTemplate()
+*         l_int32             recogAverageSamples()
+*         l_int32             pixaAccumulateSamples()
+*         l_int32             recogTrainingFinished()
+*         static l_int32      recogTemplatesAreOK()
+*         PIXA               *recogFilterPixaBySize()
+*         PIXAA              *recogSortPixaByClass()
+*         l_int32             recogRemoveOutliers1()
+*         PIXA               *pixaRemoveOutliers1()
+*         l_int32             recogRemoveOutliers2()
+*         PIXA               *pixaRemoveOutliers2()
+*
+*      Training on unlabeled data
+*         L_RECOG             recogTrainFromBoot()
+*
+*      Padding the digit training set
+*         l_int32             recogPadDigitTrainingSet()
+*         l_int32             recogIsPaddingNeeded()
+*         static SARRAY      *recogAddMissingClassStrings()
+*         PIXA               *recogAddDigitPadTemplates()
+*         static l_int32      recogCharsetAvailable()
+*
+*      Making a boot digit recognizer
+*         L_RECOG            *recogMakeBootDigitRecog()
+*         PIXA               *recogMakeBootDigitTemplates()
+*
+*      Debugging
+*         l_int32             recogShowContent()
+*         l_int32             recogDebugAverages()
+*         l_int32             recogShowAverageTemplates()
+*         static PIX         *pixDisplayOutliers()
+*         PIX                *recogDisplayOutlier()
+*         PIX                *recogShowMatchesInRange()
+*         PIX                *recogShowMatch()
+*
+*  These abbreviations are for the type of template to be used:
+*    * SI (for the scanned images)
+*    * WNL (for width-normalized lines, formed by first skeletonizing
+*           the scanned images, and then dilating to a fixed width)
+*  These abbreviations are for the type of recognizer:
+*    * BAR (book-adapted recognizer; the best type; can do identification
+*           with unscaled images and separation of touching characters.
+*    * BSR (bootstrap recognizer; used if more labeled templates are
+*           required for a BAR, either for finding more templates from
+*           the book, or making a hybrid BAR/BSR.
+*
+*  The recog struct typically holds two versions of the input templates
+*  (e.g. from a pixa) that were used to generate it.  One version is
+*  the unscaled input templates.  The other version is the one that
+*  will be used by the recog to identify unlabeled data.  That version
+*  depends on the input parameters when the recog is created.  The choices
+*  for the latter version, and their suggested use, are:
+*  (1) unscaled SI -- typical for BAR, generated from book images
+*  (2) unscaled WNL -- ditto
+*  (3) scaled SI -- typical for recognizers containing template
+*      images from sources other than the book to be recognized
+*  (4) scaled WNL -- ditto
+*  For cases (3) and (4), we recommend scaling to fixed height; e.g.,
+*  scalew = 0, scaleh = 40.
+*  When using WNL, we recommend using a width of 5 in the template
+*  and 4 in the unlabeled data.
+*  It appears that better results for a BAR are usually obtained using
+*  SI than WNL, but more experimentation is needed.
+*
+*  This utility is designed to build recognizers that are specifically
+*  adapted from a large amount of material, such as a book.  These
+*  use labeled templates taken from the material, and not scaled.
+*  In addition, two special recognizers are useful:
+*  (1) Bootstrap recognizer (BSR).  This uses height-scaled templates,
+*      that have been extended with several repetitions in one of two ways:
+*      (a) aniotropic width scaling (for either SI or WNL)
+*      (b) iterative erosions/dilations (for SI).
+*  (2) Outlier removal.  This uses height scaled templates.  It can be
+*      implemented without using templates that are aligned averages of all
+*      templates in a class.
+*
+*  Recognizers are inexpensive to generate, for example, from a pixa
+*  of labeled templates.  The general process of building a BAR is
+*  to start with labeled templates, e.g., in a pixa, make a BAR, and
+*  analyze new samples from the book to augment the BAR until it has
+*  enough samples for each character class.  Along the way, samples
+*  from a BSR may be added for help in training.  If not enough samples
+*  are available for the BAR, it can finally be augmented with BSR
+*  samples, in which case the resulting hybrid BAR/BSR recognizer
+*  must work on scaled images.
+*
+*  Here are the steps in doing recog training:
+*  A. Generate a BAR from any existing labeled templates
+*    (1) Create a recog and add the templates, using recogAddSample().
+*        This stores the unscaled templates.
+*        [Note: this can be done in one step if the labeled templates are put
+*         into a pixa:
+*           L_Recog *rec = recogCreateFromPixa(pixa, ...);  ]
+*    (2) Call recogTrainingFinished() to generate the (sometimes modified)
+*        templates to be used for correlation.
+*    (3) Optionally, remove outliers.
+*    If there are sufficient samples in the classes, we're done. Otherwise,
+*  B. Try to get more samples from the book to pad the BAR.
+*     (1) Save the unscaled, labeled templates from the BAR.
+*     (2) Supplement the BAR with bootstrap templates to make a hybrid BAR/BSR.
+*     (3) Do recognition on more unlabeled images, scaled to a fixed height
+*     (4) Add the unscaled, labeled images to the saved set.
+*     (5) Optionally, remove outliers.
+*     If there are sufficient samples in the classes, we're done. Otherwise,
+*  C. For classes without a sufficient number of templates, we can
+*     supplement the BAR with templates from a BSR (a hybrid RAR/BSR),
+*     and do recognition scaled to a fixed height.
+*
+*  Here are several methods that can be used for identifying outliers:
+*  (1) Compute average templates for each class and remove a candidate
+*      that is poorly correlated with the average.  This is the most
+*      simple method.  recogRemoveOutliers1() uses this, supplemented with
+*      a second threshold and a target number of templates to be saved.
+*  (2) Compute average templates for each class and remove a candidate
+*      that is more highly correlated with the average of some other class.
+*      This does not require setting a threshold for the correlation.
+*      recogRemoveOutliers2() uses this method, supplemented with a minimum
+*      correlation score.
+*  (3) For each candidate, find the average correlation with other
+*      members of its class, and remove those that have a relatively
+*      low average correlation.  This is similar to (1), gives comparable
+*      results and because it does not use average templates, it requires
+*      a bit more computation.
+* </pre>
+*/
+#ifdef HAVE_CONFIG_H
+#include <config_auto.h>
+#endif  /* HAVE_CONFIG_H */
+#include <string.h>
+#include "allheaders.h"
+#include "pix_internal.h"
+/* Static functions */
+static l_int32 recogTemplatesAreOK(L_RECOG *recog, l_int32 minsize,
+l_float32 minfract, l_int32 *pok);
+static SARRAY *recogAddMissingClassStrings(L_RECOG  *recog);
+static l_int32 recogCharsetAvailable(l_int32 type);
+static PIX *pixDisplayOutliers(PIXA *pixas, NUMA *nas);
+static PIX *recogDisplayOutlier(L_RECOG *recog, l_int32 iclass, l_int32 jsamp,
+l_int32 maxclass, l_float32 maxscore);
+/* Default parameters that are used in recogTemplatesAreOK() and
+* in outlier removal functions, and that use template set size
+* to decide if the set of templates (before outliers are removed)
+* is valid.  Values are set to accept most sets of sample templates. */
+static const l_int32    DefaultMinSetSize = 1;  /* minimum number of
+samples for a valid class */
+static const l_float32  DefaultMinSetFract = 0.4f;  /* minimum fraction
+of classes required for a valid recog */
+/* Defaults in pixaRemoveOutliers1() and pixaRemoveOutliers2() */
+static const l_float32  DefaultMinScore = 0.75; /* keep everything above */
+static const l_int32    DefaultMinTarget = 3;  /* to be kept if possible */
+static const l_float32  LowerScoreThreshold = 0.5;  /* templates can be
+* kept down to this score to if needed to retain the
+* desired minimum number of templates */
+/*------------------------------------------------------------------------*
+*                                Training                                *
+*------------------------------------------------------------------------*/
+/*!
+* \brief   recogTrainLabeled()
+*
+* \param[in]    recog     in training mode
+* \param[in]    pixs      if depth > 1, will be thresholded to 1 bpp
+* \param[in]    box       [optional] cropping box
+* \param[in]    text      [optional] if null, use text field in pix
+* \param[in]    debug     1 to display images of samples not captured
+* \return  0 if OK, 1 on error
+*
+* <pre>
+* Notes:
+*      (1) Training is restricted to the addition of a single
+*          character in an arbitrary (e.g., UTF8) charset
+*      (2) If box != null, it should represent the location in %pixs
+*          of the character image.
+* </pre>
+*/
+l_ok
+recogTrainLabeled(L_RECOG  *recog,
+PIX      *pixs,
+BOX      *box,
+char     *text,
+l_int32   debug)
+{
+l_int32  ret;
+PIX     *pix;
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+if (!pixs)
+return ERROR_INT("pixs not defined", __func__, 1);
+/* Prepare the sample to be added. This step also acts
+* as a filter, and can invalidate pixs as a template. */
+ret = recogProcessLabeled(recog, pixs, box, text, &pix);
+if (ret) {
+pixDestroy(&pix);
+L_WARNING("failure to get sample '%s' for training\n", __func__,
+text);
+return 1;
+}
+recogAddSample(recog, pix, debug);
+pixDestroy(&pix);
+return 0;
+}
+/*!
+* \brief   recogProcessLabeled()
+*
+* \param[in]    recog   in training mode
+* \param[in]    pixs    if depth > 1, will be thresholded to 1 bpp
+* \param[in]    box     [optional] cropping box
+* \param[in]    text    [optional] if null, use text field in pix
+* \param[out]   ppix    addr of pix, 1 bpp, labeled
+* \return  0 if OK, 1 on error
+*
+* <pre>
+* Notes:
+*      (1) This crops and binarizes the input image, generating a pix
+*          of one character where the charval is inserted into the pix.
+* </pre>
+*/
+l_ok
+recogProcessLabeled(L_RECOG  *recog,
+PIX      *pixs,
+BOX      *box,
+char     *text,
+PIX     **ppix)
+{
+char    *textdata;
+l_int32  textinpix, textin, nsets;
+NUMA    *na;
+PIX     *pix1, *pix2, *pix3, *pix4;
+if (!ppix)
+return ERROR_INT("&pix not defined", __func__, 1);
+*ppix = NULL;
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+if (!pixs)
+return ERROR_INT("pixs not defined", __func__, 1);
+/* Find the text; this will be stored with the output images */
+textin = text && (text[0] != '\0');
+textinpix = (pixs->text && (pixs->text[0] != '\0'));
+if (!textin && !textinpix) {
+L_ERROR("no text: %d\n", __func__, recog->num_samples);
+return 1;
+}
+textdata = (textin) ? text : pixs->text;  /* do not free */
+/* Crop and binarize if necessary */
+if (box)
+pix1 = pixClipRectangle(pixs, box, NULL);
+else
+pix1 = pixClone(pixs);
+if (pixGetDepth(pix1) > 1)
+pix2 = pixConvertTo1(pix1, recog->threshold);
+else
+pix2 = pixClone(pix1);
+pixDestroy(&pix1);
+/* Remove isolated noise, using as a criterion all components
+* that are removed by a vertical opening of size 5. */
+pix3 = pixMorphSequence(pix2, "o1.5", 0);  /* seed */
+pixSeedfillBinary(pix3, pix3, pix2, 8);  /* fill from seed; clip to pix2 */
+pixDestroy(&pix2);
+/* Clip to foreground */
+pixClipToForeground(pix3, &pix4, NULL);
+pixDestroy(&pix3);
+if (!pix4)
+return ERROR_INT("pix4 is empty", __func__, 1);
+/* Verify that if there is more than 1 c.c., they all have
+* horizontal overlap */
+na = pixCountByColumn(pix4, NULL);
+numaCountNonzeroRuns(na, &nsets);
+numaDestroy(&na);
+if (nsets > 1) {
+L_WARNING("found %d sets of horiz separated c.c.; skipping\n",
+__func__, nsets);
+pixDestroy(&pix4);
+return 1;
+}
+pixSetText(pix4, textdata);
+*ppix = pix4;
+return 0;
+}
+/*!
+* \brief   recogAddSample()
+*
+* \param[in]    recog
+* \param[in]    pix         a single character, 1 bpp
+* \param[in]    debug
+* \return  0 if OK, 1 on error
+*
+* <pre>
+* Notes:
+*      (1) The pix is 1 bpp, with the character string label embedded.
+*      (2) The pixaa_u array of the recog is initialized to accept
+*          up to 256 different classes.  When training is finished,
+*          the arrays are truncated to the actual number of classes.
+*          To pad an existing recog from the boot recognizers, training
+*          is started again; if samples from a new class are added,
+*          the pixaa_u array is extended by adding a pixa to hold them.
+* </pre>
+*/
+l_ok
+recogAddSample(L_RECOG  *recog,
+PIX      *pix,
+l_int32   debug)
+{
+char    *text;
+l_int32  npa, charint, index;
+PIXA    *pixa1;
+PIXAA   *paa;
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+if (!pix || pixGetDepth(pix) != 1)
+return ERROR_INT("pix not defined or not 1 bpp\n", __func__, 1);
+if (recog->train_done)
+return ERROR_INT("not added: training has been completed", __func__, 1);
+paa = recog->pixaa_u;
+/* Make sure the character is in the set */
+text = pixGetText(pix);
+if (l_convertCharstrToInt(text, &charint) == 1) {
+L_ERROR("invalid text: %s\n", __func__, text);
+return 1;
+}
+/* Determine the class array index.  Check if the class
+* already exists, and if not, add it. */
+if (recogGetClassIndex(recog, charint, text, &index) == 1) {
+/* New class must be added */
+npa = pixaaGetCount(paa, NULL);
+if (index > npa) {
+L_ERROR("oops: bad index %d > npa %d!!\n", __func__, index, npa);
+return 1;
+}
+if (index == npa) {  /* paa needs to be extended */
+L_INFO("Adding new class and pixa: index = %d, text = %s\n",
+__func__, index, text);
+pixa1 = pixaCreate(10);
+pixaaAddPixa(paa, pixa1, L_INSERT);
+}
+}
+if (debug) {
+L_INFO("Identified text label: %s\n", __func__, text);
+L_INFO("Identified: charint = %d, index = %d\n",
+__func__, charint, index);
+}
+/* Insert the unscaled character image into the right pixa.
+* (Unscaled images are required to split touching characters.) */
+recog->num_samples++;
+pixaaAddPix(paa, index, pix, NULL, L_COPY);
+return 0;
+}
+/*!
+* \brief   recogModifyTemplate()
+*
+* \param[in]    recog
+* \param[in]    pixs   1 bpp, to be optionally scaled and turned into
+*                      strokes of fixed width
+* \return  pixd   modified pix if OK, NULL on error
+*/
+PIX *
+recogModifyTemplate(L_RECOG  *recog,
+PIX      *pixs)
+{
+l_int32  w, h, empty;
+PIX     *pix1, *pix2;
+if (!recog)
+return (PIX *)ERROR_PTR("recog not defined", __func__, NULL);
+if (!pixs)
+return (PIX *)ERROR_PTR("pixs not defined", __func__, NULL);
+/* Scale first */
+pixGetDimensions(pixs, &w, &h, NULL);
+if ((recog->scalew == 0 || recog->scalew == w) &&
+(recog->scaleh == 0 || recog->scaleh == h)) {  /* no scaling */
+pix1 = pixCopy(NULL, pixs);
+} else {
+pix1 = pixScaleToSize(pixs, recog->scalew, recog->scaleh);
+}
+if (!pix1)
+return (PIX *)ERROR_PTR("pix1 not made", __func__, NULL);
+/* Then optionally convert to lines */
+if (recog->linew <= 0) {
+pix2 = pixClone(pix1);
+} else {
+pix2 = pixSetStrokeWidth(pix1, recog->linew, 1, 8);
+}
+pixDestroy(&pix1);
+if (!pix2)
+return (PIX *)ERROR_PTR("pix2 not made", __func__, NULL);
+/* Make sure we still have some pixels */
+pixZero(pix2, &empty);
+if (empty) {
+pixDestroy(&pix2);
+return (PIX *)ERROR_PTR("modified template has no pixels",
+__func__, NULL);
+}
+return pix2;
+}
+/*!
+* \brief   recogAverageSamples()
+*
+* \param[in]   recog    addr of existing recog
+* \param[in]   debug
+* \return  0 on success, 1 on failure
+*
+* <pre>
+* Notes:
+*      (1) This is only called in two situations:
+*          (a) When splitting characters using either the DID method
+*              recogDecode() or the the greedy splitter
+*              recogCorrelationBestRow()
+*          (b) By a special recognizer that is used to remove outliers.
+*          Both unscaled and scaled inputs are averaged.
+*      (2) If the data in any class is nonexistent (no samples), or
+*          very bad (no fg pixels in the average), or if the ratio
+*          of max/min average unscaled class template heights is
+*          greater than max_ht_ratio, this function fails.  The caller
+*          must check the return value of the recog, and destroy the
+*          recog on failure.
+*      (3) Set debug = 1 to view the resulting templates and their centroids.
+* </pre>
+*/
+l_int32
+recogAverageSamples(L_RECOG  *recog,
+l_int32   debug)
+{
+l_int32    i, nsamp, size, area, bx, by, badclass;
+l_float32  x, y, hratio;
+BOX       *box;
+PIXA      *pixa1;
+PIX       *pix1, *pix2, *pix3;
+PTA       *pta1;
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+if (recog->ave_done) {
+if (debug)  /* always do this if requested */
+recogShowAverageTemplates(recog);
+return 0;
+}
+/* Remove any previous averaging data */
+size = recog->setsize;
+pixaDestroy(&recog->pixa_u);
+ptaDestroy(&recog->pta_u);
+numaDestroy(&recog->nasum_u);
+recog->pixa_u = pixaCreate(size);
+recog->pta_u = ptaCreate(size);
+recog->nasum_u = numaCreate(size);
+pixaDestroy(&recog->pixa);
+ptaDestroy(&recog->pta);
+numaDestroy(&recog->nasum);
+recog->pixa = pixaCreate(size);
+recog->pta = ptaCreate(size);
+recog->nasum = numaCreate(size);
+/* Unscaled bitmaps: compute averaged bitmap, centroid, and fg area.
+* Note that when we threshold to 1 bpp the 8 bpp averaged template
+* that is returned from the accumulator, it will not be cropped
+* to the foreground.  We must crop it, because the correlator
+* makes that assumption and will return a zero value if the
+* width or height of the two images differs by several pixels.
+* But cropping to fg can cause the value of the centroid to
+* change, if bx > 0 or by > 0. */
+badclass = FALSE;
+for (i = 0; i < size; i++) {
+pixa1 = pixaaGetPixa(recog->pixaa_u, i, L_CLONE);
+pta1 = ptaaGetPta(recog->ptaa_u, i, L_CLONE);
+nsamp = pixaGetCount(pixa1);
+nsamp = L_MIN(nsamp, 256);  /* we only use the first 256 */
+if (nsamp == 0) {  /* no information for this class */
+L_ERROR("no samples in class %d\n", __func__, i);
+badclass = TRUE;
+pixaDestroy(&pixa1);
+ptaDestroy(&pta1);
+break;
+} else {
+pixaAccumulateSamples(pixa1, pta1, &pix1, &x, &y);
+pix2 = pixThresholdToBinary(pix1, L_MAX(1, nsamp / 2));
+pixInvert(pix2, pix2);
+pixClipToForeground(pix2, &pix3, &box);
+if (!box) {
+L_ERROR("no fg pixels in average for uclass %d\n", __func__, i);
+badclass = TRUE;
+pixDestroy(&pix1);
+pixDestroy(&pix2);
+pixaDestroy(&pixa1);
+ptaDestroy(&pta1);
+break;
+} else {
+boxGetGeometry(box, &bx, &by, NULL, NULL);
+pixaAddPix(recog->pixa_u, pix3, L_INSERT);
+ptaAddPt(recog->pta_u, x - bx, y - by);  /* correct centroid */
+pixCountPixels(pix3, &area, recog->sumtab);
+numaAddNumber(recog->nasum_u, area);  /* foreground */
+boxDestroy(&box);
+}
+pixDestroy(&pix1);
+pixDestroy(&pix2);
+}
+pixaDestroy(&pixa1);
+ptaDestroy(&pta1);
+}
+/* Are any classes bad?  */
+if (badclass)
+return ERROR_INT("at least 1 bad class", __func__, 1);
+/* Get the range of sizes of the unscaled average templates.
+* Reject if the height ratio is too large.  */
+pixaSizeRange(recog->pixa_u, &recog->minwidth_u, &recog->minheight_u,
+&recog->maxwidth_u, &recog->maxheight_u);
+hratio = (l_float32)recog->maxheight_u / (l_float32)recog->minheight_u;
+if (hratio > recog->max_ht_ratio) {
+L_ERROR("ratio of max/min height of average templates = %4.1f\n",
+__func__, hratio);
+return 1;
+}
+/* Scaled bitmaps: compute averaged bitmap, centroid, and fg area */
+for (i = 0; i < size; i++) {
+pixa1 = pixaaGetPixa(recog->pixaa, i, L_CLONE);
+pta1 = ptaaGetPta(recog->ptaa, i, L_CLONE);
+nsamp = pixaGetCount(pixa1);
+nsamp = L_MIN(nsamp, 256);  /* we only use the first 256 */
+pixaAccumulateSamples(pixa1, pta1, &pix1, &x, &y);
+pix2 = pixThresholdToBinary(pix1, L_MAX(1, nsamp / 2));
+pixInvert(pix2, pix2);
+pixClipToForeground(pix2, &pix3, &box);
+if (!box) {
+L_ERROR("no fg pixels in average for class %d\n", __func__, i);
+badclass = TRUE;
+pixDestroy(&pix1);
+pixDestroy(&pix2);
+pixaDestroy(&pixa1);
+ptaDestroy(&pta1);
+break;
+} else {
+boxGetGeometry(box, &bx, &by, NULL, NULL);
+pixaAddPix(recog->pixa, pix3, L_INSERT);
+ptaAddPt(recog->pta, x - bx, y - by);  /* correct centroid */
+pixCountPixels(pix3, &area, recog->sumtab);
+numaAddNumber(recog->nasum, area);  /* foreground */
+boxDestroy(&box);
+}
+pixDestroy(&pix1);
+pixDestroy(&pix2);
+pixaDestroy(&pixa1);
+ptaDestroy(&pta1);
+}
+if (badclass)
+return ERROR_INT("no fg pixels in at least 1 class", __func__, 1);
+/* Get the range of widths of the scaled average templates */
+pixaSizeRange(recog->pixa, &recog->minwidth, NULL, &recog->maxwidth, NULL);
+/* Get dimensions useful for splitting */
+recog->min_splitw = L_MAX(5, recog->minwidth_u - 5);
+recog->max_splith = recog->maxheight_u + 12;  /* allow for skew */
+if (debug)
+recogShowAverageTemplates(recog);
+recog->ave_done = TRUE;
+return 0;
+}
+/*!
+* \brief   pixaAccumulateSamples()
+*
+* \param[in]    pixa     of samples from the same class, 1 bpp
+* \param[in]    pta      [optional] of centroids of the samples
+* \param[out]   ppixd    accumulated samples, 8 bpp
+* \param[out]   px       [optional] average x coordinate of centroids
+* \param[out]   py       [optional] average y coordinate of centroids
+* \return  0 on success, 1 on failure
+*
+* <pre>
+* Notes:
+*      (1) This generates an aligned (by centroid) sum of the input pix.
+*      (2) We use only the first 256 samples; that's plenty.
+*      (3) If pta is not input, we generate two tables, and discard
+*          after use.  If this is called many times, it is better
+*          to precompute the pta.
+* </pre>
+*/
+l_int32
+pixaAccumulateSamples(PIXA       *pixa,
+PTA        *pta,
+PIX       **ppixd,
+l_float32  *px,
+l_float32  *py)
+{
+l_int32    i, n, maxw, maxh, xdiff, ydiff;
+l_int32   *centtab, *sumtab;
+l_float32  xc, yc, xave, yave;
+PIX       *pix1, *pix2, *pixsum;
+PTA       *ptac;
+if (px) *px = 0;
+if (py) *py = 0;
+if (!ppixd)
+return ERROR_INT("&pixd not defined", __func__, 1);
+*ppixd = NULL;
+if (!pixa)
+return ERROR_INT("pixa not defined", __func__, 1);
+n = pixaGetCount(pixa);
+if (pta && ptaGetCount(pta) != n)
+return ERROR_INT("pta count differs from pixa count", __func__, 1);
+n = L_MIN(n, 256);  /* take the first 256 only */
+if (n == 0)
+return ERROR_INT("pixa array empty", __func__, 1);
+/* Find the centroids */
+if (pta) {
+ptac = ptaClone(pta);
+} else {  /* generate them here */
+ptac = ptaCreate(n);
+centtab = makePixelCentroidTab8();
+sumtab = makePixelSumTab8();
+for (i = 0; i < n; i++) {
+pix1 = pixaGetPix(pixa, i, L_CLONE);
+pixCentroid(pix1, centtab, sumtab, &xc, &yc);
+ptaAddPt(ptac, xc, yc);
+}
+LEPT_FREE(centtab);
+LEPT_FREE(sumtab);
+}
+/* Find the average value of the centroids */
+xave = yave = 0;
+for (i = 0; i < n; i++) {
+ptaGetPt(pta, i, &xc, &yc);
+xave += xc;
+yave += yc;
+}
+xave = xave / (l_float32)n;
+yave = yave / (l_float32)n;
+if (px) *px = xave;
+if (py) *py = yave;
+/* Place all pix with their centroids located at the average
+* centroid value, and sum the results.  Make the accumulator
+* image slightly larger than the largest sample to insure
+* that all pixels are represented in the accumulator.  */
+pixaSizeRange(pixa, NULL, NULL, &maxw, &maxh);
+pixsum = pixInitAccumulate(maxw + 5, maxh + 5, 0);
+pix1 = pixCreate(maxw, maxh, 1);
+for (i = 0; i < n; i++) {
+pix2 = pixaGetPix(pixa, i, L_CLONE);
+ptaGetPt(ptac, i, &xc, &yc);
+xdiff = (l_int32)(xave - xc);
+ydiff = (l_int32)(yave - yc);
+pixClearAll(pix1);
+pixRasterop(pix1, xdiff, ydiff, maxw, maxh, PIX_SRC,
+pix2, 0, 0);
+pixAccumulate(pixsum, pix1, L_ARITH_ADD);
+pixDestroy(&pix2);
+}
+*ppixd = pixFinalAccumulate(pixsum, 0, 8);
+pixDestroy(&pix1);
+pixDestroy(&pixsum);
+ptaDestroy(&ptac);
+return 0;
+}
+/*!
+* \brief   recogTrainingFinished()
+*
+* \param[in]    precog       addr of recog
+* \param[in]    modifyflag   1 to use recogModifyTemplate(); 0 otherwise
+* \param[in]    minsize      set to -1 for default
+* \param[in]    minfract     set to -1.0 for default
+* \return  0 if OK, 1 on error (input recog will be destroyed)
+*
+* <pre>
+* Notes:
+*      (1) This must be called after all training samples have been added.
+*      (2) If the templates are not good enough, the recog input is destroyed.
+*      (3) Usually, %modifyflag == 1, because we want to apply
+*          recogModifyTemplate() to generate the actual templates
+*          that will be used.  The one exception is when reading a
+*          serialized recog: there we want to put the same set of
+*          templates in both the unscaled and modified pixaa.
+*          See recogReadStream() to see why we do this.
+*      (4) See recogTemplatesAreOK() for %minsize and %minfract usage.
+*      (5) The following things are done here:
+*          (a) Allocate (or reallocate) storage for (possibly) modified
+*              bitmaps, centroids, and fg areas.
+*          (b) Generate the (possibly) modified bitmaps.
+*          (c) Compute centroid and fg area data for both unscaled and
+*              modified bitmaps.
+*          (d) Truncate the pixaa, ptaa and numaa arrays down from
+*              256 to the actual size.
+*      (6) Putting these operations here makes it simple to recompute
+*          the recog with different modifications on the bitmaps.
+*      (7) Call recogShowContent() to display the templates, both
+*          unscaled and modified.
+* </pre>
+*/
+l_ok
+recogTrainingFinished(L_RECOG  **precog,
+l_int32    modifyflag,
+l_int32    minsize,
+l_float32  minfract)
+{
+l_int32    ok, i, j, size, nc, ns, area;
+l_float32  xave, yave;
+PIX       *pix, *pixd;
+PIXA      *pixa;
+PIXAA     *paa;
+PTA       *pta;
+PTAA      *ptaa;
+L_RECOG   *recog;
+if (!precog)
+return ERROR_INT("&recog not defined", __func__, 1);
+if ((recog = *precog) == NULL)
+return ERROR_INT("recog not defined", __func__, 1);
+if (recog->train_done) return 0;
+/* Test the input templates */
+recogTemplatesAreOK(recog, minsize, minfract, &ok);
+if (!ok) {
+recogDestroy(precog);
+return ERROR_INT("bad templates", __func__, 1);
+}
+/* Generate the storage for the possibly-scaled training bitmaps */
+size = recog->maxarraysize;
+paa = pixaaCreate(size);
+pixa = pixaCreate(1);
+pixaaInitFull(paa, pixa);
+pixaDestroy(&pixa);
+pixaaDestroy(&recog->pixaa);
+recog->pixaa = paa;
+/* Generate the storage for the unscaled centroid training data */
+ptaa = ptaaCreate(size);
+pta = ptaCreate(0);
+ptaaInitFull(ptaa, pta);
+ptaaDestroy(&recog->ptaa_u);
+recog->ptaa_u = ptaa;
+/* Generate the storage for the possibly-scaled centroid data */
+ptaa = ptaaCreate(size);
+ptaaInitFull(ptaa, pta);
+ptaDestroy(&pta);
+ptaaDestroy(&recog->ptaa);
+recog->ptaa = ptaa;
+/* Generate the storage for the fg area data */
+numaaDestroy(&recog->naasum_u);
+numaaDestroy(&recog->naasum);
+recog->naasum_u = numaaCreateFull(size, 0);
+recog->naasum = numaaCreateFull(size, 0);
+paa = recog->pixaa_u;
+nc = recog->setsize;
+for (i = 0; i < nc; i++) {
+pixa = pixaaGetPixa(paa, i, L_CLONE);
+ns = pixaGetCount(pixa);
+for (j = 0; j < ns; j++) {
+/* Save centroid and area data for the unscaled pix */
+pix = pixaGetPix(pixa, j, L_CLONE);
+pixCentroid(pix, recog->centtab, recog->sumtab, &xave, &yave);
+ptaaAddPt(recog->ptaa_u, i, xave, yave);
+pixCountPixels(pix, &area, recog->sumtab);
+numaaAddNumber(recog->naasum_u, i, area);  /* foreground */
+/* Insert the (optionally) scaled character image, and
+* save centroid and area data for it */
+if (modifyflag == 1)
+pixd = recogModifyTemplate(recog, pix);
+else
+pixd = pixClone(pix);
+if (pixd) {
+pixaaAddPix(recog->pixaa, i, pixd, NULL, L_INSERT);
+pixCentroid(pixd, recog->centtab, recog->sumtab, &xave, &yave);
+ptaaAddPt(recog->ptaa, i, xave, yave);
+pixCountPixels(pixd, &area, recog->sumtab);
+numaaAddNumber(recog->naasum, i, area);
+} else {
+L_ERROR("failed: modified template for class %d, sample %d\n",
+__func__, i, j);
+}
+pixDestroy(&pix);
+}
+pixaDestroy(&pixa);
+}
+/* Truncate the arrays to those with non-empty containers */
+pixaaTruncate(recog->pixaa_u);
+pixaaTruncate(recog->pixaa);
+ptaaTruncate(recog->ptaa_u);
+ptaaTruncate(recog->ptaa);
+numaaTruncate(recog->naasum_u);
+numaaTruncate(recog->naasum);
+recog->train_done = TRUE;
+return 0;
+}
+/*!
+* \brief   recogTemplatesAreOK()
+*
+* \param[in]    recog
+* \param[in]    minsize     set to -1 for default
+* \param[in]    minfract    set to -1.0 for default
+* \param[out]   pok         set to 1 if template set is valid; 0 otherwise
+* \return  1 on error; 0 otherwise.  An invalid template set is not an error.
+*
+* <pre>
+* Notes:
+*      (1) This is called by recogTrainingFinished().  A return value of 0
+*          will cause recogTrainingFinished() to destroy the recog.
+*      (2) %minsize is the minimum number of samples required for
+*          the class; -1 uses the default
+*      (3) %minfract is the minimum fraction of classes required for
+*          the recog to be usable; -1.0 uses the default
+* </pre>
+*/
+static l_int32
+recogTemplatesAreOK(L_RECOG   *recog,
+l_int32    minsize,
+l_float32  minfract,
+l_int32   *pok)
+{
+l_int32    i, n, validsets, nt;
+l_float32  ratio;
+NUMA      *na;
+if (!pok)
+return ERROR_INT("&ok not defined", __func__, 1);
+*pok = 0;
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+minsize = (minsize < 0) ? DefaultMinSetSize : minsize;
+minfract = (minfract < 0) ? DefaultMinSetFract : minfract;
+n = pixaaGetCount(recog->pixaa_u, &na);
+validsets = 0;
+for (i = 0, validsets = 0; i < n; i++) {
+numaGetIValue(na, i, &nt);
+if (nt >= minsize)
+validsets++;
+}
+numaDestroy(&na);
+ratio = (l_float32)validsets / (l_float32)recog->charset_size;
+*pok = (ratio >= minfract) ? 1 : 0;
+return 0;
+}
+/*!
+* \brief   recogFilterPixaBySize()
+*
+* \param[in]   pixas         labeled templates
+* \param[in]   setsize       size of character set (number of classes)
+* \param[in]   maxkeep       max number of templates to keep in a class
+* \param[in]   max_ht_ratio  max allowed height ratio (see below)
+* \param[out]  pna           [optional] debug output, giving the number
+*                            in each class after filtering; use NULL to skip
+* \return  pixa   filtered templates, or NULL on error
+*
+* <pre>
+* Notes:
+*      (1) The basic assumption is that the most common and larger
+*          templates in each class are more likely to represent the
+*          characters we are interested in.  For example, larger digits
+*          are more likely to represent page numbers, and smaller digits
+*          could be data in tables.  Therefore, we bias the first
+*          stage of filtering toward the larger characters by removing
+*          very small ones, and select based on proximity of the
+*          remaining characters to median height.
+*      (2) For each of the %setsize classes, order the templates
+*          increasingly by height.  Take the rank 0.9 height.  Eliminate
+*          all templates that are shorter by more than %max_ht_ratio.
+*          Of the remaining ones, select up to %maxkeep that are closest
+*          in rank order height to the median template.
+* </pre>
+*/
+PIXA *
+recogFilterPixaBySize(PIXA      *pixas,
+l_int32    setsize,
+l_int32    maxkeep,
+l_float32  max_ht_ratio,
+NUMA     **pna)
+{
+l_int32    i, j, h90, hj, j1, j2, j90, n, nc;
+l_float32  ratio;
+NUMA      *na;
+PIXA      *pixa1, *pixa2, *pixa3, *pixa4, *pixa5;
+PIXAA     *paa;
+if (pna) *pna = NULL;
+if (!pixas)
+return (PIXA *)ERROR_PTR("pixas not defined", __func__, NULL);
+if ((paa = recogSortPixaByClass(pixas, setsize)) == NULL)
+return (PIXA *)ERROR_PTR("paa not made", __func__, NULL);
+nc = pixaaGetCount(paa, NULL);
+na = (pna) ? numaCreate(0) : NULL;
+if (pna) *pna = na;
+pixa5 = pixaCreate(0);
+for (i = 0; i < nc; i++) {
+pixa1 = pixaaGetPixa(paa, i, L_CLONE);
+if ((n = pixaGetCount(pixa1)) == 0) {
+pixaDestroy(&pixa1);
+continue;
+}
+pixa2 = pixaSort(pixa1, L_SORT_BY_HEIGHT, L_SORT_INCREASING, NULL,
+L_COPY);
+j90 = (l_int32)(0.9 * n);
+pixaGetPixDimensions(pixa2, j90, NULL, &h90, NULL);
+pixa3 = pixaCreate(n);
+for (j = 0; j < n; j++) {
+pixaGetPixDimensions(pixa2, j, NULL, &hj, NULL);
+ratio = (l_float32)h90 / (l_float32)hj;
+if (ratio <= max_ht_ratio)
+pixaAddPix(pixa3, pixaGetPix(pixa2, j, L_COPY), L_INSERT);
+}
+n = pixaGetCount(pixa3);
+if (n <= maxkeep) {
+pixa4 = pixaCopy(pixa3, L_CLONE);
+} else {
+j1 = (n - maxkeep) / 2;
+j2 = j1 + maxkeep - 1;
+pixa4 = pixaSelectRange(pixa3, j1, j2, L_CLONE);
+}
+if (na) numaAddNumber(na, pixaGetCount(pixa4));
+pixaJoin(pixa5, pixa4, 0, -1);
+pixaDestroy(&pixa1);
+pixaDestroy(&pixa2);
+pixaDestroy(&pixa3);
+pixaDestroy(&pixa4);
+}
+pixaaDestroy(&paa);
+return pixa5;
+}
+/*!
+* \brief   recogSortPixaByClass()
+*
+* \param[in]   pixa       labeled templates
+* \param[in]   setsize    size of character set (number of classes)
+* \return  paa   pixaa where each pixa has templates for one class,
+*                or null on error
+*/
+PIXAA *
+recogSortPixaByClass(PIXA    *pixa,
+l_int32  setsize)
+{
+PIXAA    *paa;
+L_RECOG  *recog;
+if (!pixa)
+return (PIXAA *)ERROR_PTR("pixa not defined", __func__, NULL);
+if ((recog = recogCreateFromPixaNoFinish(pixa, 0, 0, 0, 0, 0)) == NULL)
+return (PIXAA *)ERROR_PTR("recog not made", __func__, NULL);
+paa = recog->pixaa_u;   /* grab the paa of unscaled templates */
+recog->pixaa_u = NULL;
+recogDestroy(&recog);
+return paa;
+}
+/*!
+* \brief   recogRemoveOutliers1()
+*
+* \param[in]   precog       addr of recog with unscaled labeled templates
+* \param[in]   minscore     keep everything with at least this score
+* \param[in]   mintarget    minimum desired number to retain if possible
+* \param[in]   minsize      minimum number of samples required for a class
+* \param[out]  ppixsave     [optional debug] saved templates, with scores
+* \param[out]  ppixrem      [optional debug] removed templates, with scores
+* \return  0 if OK, 1 on error.
+*
+* <pre>
+* Notes:
+*      (1) This is a convenience wrapper when using default parameters
+*          for the recog.  See pixaRemoveOutliers1() for details.
+*      (2) If this succeeds, the new recog replaces the input recog;
+*          if it fails, the input recog is destroyed.
+* </pre>
+*/
+l_ok
+recogRemoveOutliers1(L_RECOG  **precog,
+l_float32  minscore,
+l_int32    mintarget,
+l_int32    minsize,
+PIX      **ppixsave,
+PIX      **ppixrem)
+{
+PIXA     *pixa1, *pixa2;
+L_RECOG  *recog;
+if (!precog)
+return ERROR_INT("&recog not defined", __func__, 1);
+if (*precog == NULL)
+return ERROR_INT("recog not defined", __func__, 1);
+/* Extract the unscaled templates */
+pixa1 = recogExtractPixa(*precog);
+recogDestroy(precog);
+pixa2 = pixaRemoveOutliers1(pixa1, minscore, mintarget, minsize,
+ppixsave, ppixrem);
+pixaDestroy(&pixa1);
+if (!pixa2)
+return ERROR_INT("failure to remove outliers", __func__, 1);
+recog = recogCreateFromPixa(pixa2, 0, 0, 0, 150, 1);
+pixaDestroy(&pixa2);
+if (!recog)
+return ERROR_INT("failure to make recog from pixa sans outliers",
+__func__, 1);
+*precog = recog;
+return 0;
+}
+/*!
+* \brief   pixaRemoveOutliers1()
+*
+* \param[in]   pixas        unscaled labeled templates
+* \param[in]   minscore     keep everything with at least this score;
+*                           use -1.0 for default.
+* \param[in]   mintarget    minimum desired number to retain if possible;
+*                           use -1 for default.
+* \param[in]   minsize      minimum number of samples required for a class;
+*                           use -1 for default.
+* \param[out]  ppixsave     [optional debug] saved templates, with scores
+* \param[out]  ppixrem      [optional debug] removed templates, with scores
+* \return  pixa   of unscaled templates to be kept, or NULL on error
+*
+* <pre>
+* Notes:
+*      (1) Removing outliers is particularly important when recognition
+*          goes against all the samples in the training set, as opposed
+*          to the averages for each class.  The reason is that we get
+*          an identification error if a mislabeled template is a best
+*          match for an input sample.
+*      (2) Because the score values depend strongly on the quality
+*          of the character images, to avoid losing too many samples
+*          we supplement a minimum score for retention with a score
+*          necessary to acquire the minimum target number of templates.
+*          To do this we are willing to use a lower threshold,
+*          LowerScoreThreshold, on the score.  Consequently, with
+*          poor quality templates, we may keep samples with a score
+*          less than %minscore, but never less than LowerScoreThreshold.
+*          And if the number of samples is less than %minsize, we do
+*          not use any.
+*      (3) This is meant to be used on a BAR, where the templates all
+*          come from the same book; use minscore ~0.75.
+*      (4) Method: make a scaled recog from the input %pixas.  Then,
+*          for each class: generate the averages, match each
+*          scaled template against the average, and save unscaled
+*          templates that had a sufficiently good match.
+* </pre>
+*/
+PIXA *
+pixaRemoveOutliers1(PIXA      *pixas,
+l_float32  minscore,
+l_int32    mintarget,
+l_int32    minsize,
+PIX      **ppixsave,
+PIX      **ppixrem)
+{
+l_int32    i, j, debug, n, area1, area2;
+l_float32  x1, y1, x2, y2, minfract, score, rankscore, threshscore;
+NUMA      *nasum, *narem, *nasave, *nascore;
+PIX       *pix1, *pix2;
+PIXA      *pixa, *pixarem, *pixad;
+PTA       *pta;
+L_RECOG   *recog;
+if (ppixsave) *ppixsave = NULL;
+if (ppixrem) *ppixrem = NULL;
+if (!pixas)
+return (PIXA *)ERROR_PTR("pixas not defined", __func__, NULL);
+minscore = L_MIN(minscore, 1.0);
+if (minscore <= 0.0)
+minscore = DefaultMinScore;
+mintarget = L_MIN(mintarget, 3);
+if (mintarget <= 0)
+mintarget = DefaultMinTarget;
+if (minsize < 0)
+minsize = DefaultMinSetSize;
+/* Make a special height-scaled recognizer with average templates */
+debug = (ppixsave || ppixrem) ? 1 : 0;
+recog = recogCreateFromPixa(pixas, 0, 40, 0, 128, 1);
+if (!recog)
+return (PIXA *)ERROR_PTR("bad pixas; recog not made", __func__, NULL);
+if (recogAverageSamples(recog, debug) != 0) {
+recogDestroy(&recog);
+return (PIXA *)ERROR_PTR("bad templates", __func__, NULL);
+}
+nasave = (ppixsave) ? numaCreate(0) : NULL;
+pixarem = (ppixrem) ? pixaCreate(0) : NULL;
+narem = (ppixrem) ? numaCreate(0) : NULL;
+pixad = pixaCreate(0);
+for (i = 0; i < recog->setsize; i++) {
+/* Access the average template and values for scaled
+* images in this class */
+pix1 = pixaGetPix(recog->pixa, i, L_CLONE);
+ptaGetPt(recog->pta, i, &x1, &y1);
+numaGetIValue(recog->nasum, i, &area1);
+/* Get the scores for each sample in the class */
+pixa = pixaaGetPixa(recog->pixaa, i, L_CLONE);
+pta = ptaaGetPta(recog->ptaa, i, L_CLONE);  /* centroids */
+nasum = numaaGetNuma(recog->naasum, i, L_CLONE);  /* fg areas */
+n = pixaGetCount(pixa);
+nascore = numaCreate(n);
+for (j = 0; j < n; j++) {
+pix2 = pixaGetPix(pixa, j, L_CLONE);
+ptaGetPt(pta, j, &x2, &y2);  /* centroid average */
+numaGetIValue(nasum, j, &area2);  /* fg sum average */
+pixCorrelationScoreSimple(pix1, pix2, area1, area2,
+x1 - x2, y1 - y2, 5, 5,
+recog->sumtab, &score);
+numaAddNumber(nascore, score);
+if (debug && score == 0.0)  /* typ. large size difference */
+lept_stderr("Got 0 score for i = %d, j = %d\n", i, j);
+pixDestroy(&pix2);
+}
+pixDestroy(&pix1);
+/* Find the rankscore, corresponding to the 1.0 - minfract.
+* To attempt to maintain the minfract of templates, use as a
+* cutoff the minimum of minscore and the rank score.  However,
+* no template is saved with an actual score less than
+* that at least one template is kept. */
+minfract = (l_float32)mintarget / (l_float32)n;
+numaGetRankValue(nascore, 1.0 - minfract, NULL, 0, &rankscore);
+threshscore = L_MAX(LowerScoreThreshold,
+L_MIN(minscore, rankscore));
+if (debug) {
+L_INFO("minscore = %4.2f, rankscore = %4.2f, threshscore = %4.2f\n",
+__func__, minscore, rankscore, threshscore);
+}
+/* Save templates that are at or above threshold.
+* Toss any classes with less than %minsize templates. */
+for (j = 0; j < n; j++) {
+numaGetFValue(nascore, j, &score);
+pix1 = pixaaGetPix(recog->pixaa_u, i, j, L_COPY);
+if (score >= threshscore && n >= minsize) {
+pixaAddPix(pixad, pix1, L_INSERT);
+if (nasave) numaAddNumber(nasave, score);
+} else if (debug) {
+pixaAddPix(pixarem, pix1, L_INSERT);
+numaAddNumber(narem, score);
+} else {
+pixDestroy(&pix1);
+}
+}
+pixaDestroy(&pixa);
+ptaDestroy(&pta);
+numaDestroy(&nasum);
+numaDestroy(&nascore);
+}
+if (ppixsave) {
+*ppixsave = pixDisplayOutliers(pixad, nasave);
+numaDestroy(&nasave);
+}
+if (ppixrem) {
+*ppixrem = pixDisplayOutliers(pixarem, narem);
+pixaDestroy(&pixarem);
+numaDestroy(&narem);
+}
+recogDestroy(&recog);
+return pixad;
+}
+/*!
+* \brief   recogRemoveOutliers2()
+*
+* \param[in]   precog      addr of recog with unscaled labeled templates
+* \param[in]   minscore    keep everything with at least this score
+* \param[in]   minsize     minimum number of samples required for a class
+* \param[out]  ppixsave    [optional debug] saved templates, with scores
+* \param[out]  ppixrem     [optional debug] removed templates, with scores
+* \return  0 if OK, 1 on error.
+*
+* <pre>
+* Notes:
+*      (1) This is a convenience wrapper when using default parameters
+*          for the recog.  See pixaRemoveOutliers2() for details.
+*      (2) If this succeeds, the new recog replaces the input recog;
+*          if it fails, the input recog is destroyed.
+* </pre>
+*/
+l_ok
+recogRemoveOutliers2(L_RECOG  **precog,
+l_float32  minscore,
+l_int32    minsize,
+PIX      **ppixsave,
+PIX      **ppixrem)
+{
+PIXA     *pixa1, *pixa2;
+L_RECOG  *recog;
+if (!precog)
+return ERROR_INT("&recog not defined", __func__, 1);
+if (*precog == NULL)
+return ERROR_INT("recog not defined", __func__, 1);
+/* Extract the unscaled templates */
+pixa1 = recogExtractPixa(*precog);
+recogDestroy(precog);
+pixa2 = pixaRemoveOutliers2(pixa1, minscore, minsize, ppixsave, ppixrem);
+pixaDestroy(&pixa1);
+if (!pixa2)
+return ERROR_INT("failure to remove outliers", __func__, 1);
+recog = recogCreateFromPixa(pixa2, 0, 0, 0, 150, 1);
+pixaDestroy(&pixa2);
+if (!recog)
+return ERROR_INT("failure to make recog from pixa sans outliers",
+__func__, 1);
+*precog = recog;
+return 0;
+}
+/*!
+* \brief   pixaRemoveOutliers2()
+*
+* \param[in]   pixas       unscaled labeled templates
+* \param[in]   minscore    keep everything with at least this score;
+*                          use -1.0 for default.
+* \param[in]   minsize     minimum number of samples required for a class;
+*                          use -1 for default.
+* \param[out]  ppixsave    [optional debug] saved templates, with scores
+* \param[out]  ppixrem     [optional debug] removed templates, with scores
+* \return  pixa   of unscaled templates to be kept, or NULL on error
+*
+* <pre>
+* Notes:
+*      (1) Removing outliers is particularly important when recognition
+*          goes against all the samples in the training set, as opposed
+*          to the averages for each class.  The reason is that we get
+*          an identification error if a mislabeled template is a best
+*          match for an input sample.
+*      (2) This method compares each template against the average templates
+*          of each class, and discards any template that has a higher
+*          correlation to a class different from its own.  It also
+*          sets a lower bound on correlation scores with its class average.
+*      (3) This is meant to be used on a BAR, where the templates all
+*          come from the same book; use minscore ~0.75.
+* </pre>
+*/
+PIXA *
+pixaRemoveOutliers2(PIXA      *pixas,
+l_float32  minscore,
+l_int32    minsize,
+PIX      **ppixsave,
+PIX      **ppixrem)
+{
+l_int32    i, j, k, n, area1, area2, maxk, debug;
+l_float32  x1, y1, x2, y2, score, maxscore;
+NUMA      *nan, *nascore, *nasave;
+PIX       *pix1, *pix2, *pix3;
+PIXA      *pixarem, *pixad;
+L_RECOG   *recog;
+if (ppixsave) *ppixsave = NULL;
+if (ppixrem) *ppixrem = NULL;
+if (!pixas)
+return (PIXA *)ERROR_PTR("pixas not defined", __func__, NULL);
+minscore = L_MIN(minscore, 1.0);
+if (minscore <= 0.0)
+minscore = DefaultMinScore;
+if (minsize < 0)
+minsize = DefaultMinSetSize;
+/* Make a special height-scaled recognizer with average templates */
+debug = (ppixsave || ppixrem) ? 1 : 0;
+recog = recogCreateFromPixa(pixas, 0, 40, 0, 128, 1);
+if (!recog)
+return (PIXA *)ERROR_PTR("bad pixas; recog not made", __func__, NULL);
+if (recogAverageSamples(recog, debug) != 0) {
+recogDestroy(&recog);
+return (PIXA *)ERROR_PTR("bad templates", __func__, NULL);
+}
+nasave = (ppixsave) ? numaCreate(0) : NULL;
+pixarem = (ppixrem) ? pixaCreate(0) : NULL;
+pixad = pixaCreate(0);
+pixaaGetCount(recog->pixaa, &nan);  /* number of templates in each class */
+for (i = 0; i < recog->setsize; i++) {
+/* Get the scores for each sample in the class, when comparing
+* with averages from all the classes. */
+numaGetIValue(nan, i, &n);
+for (j = 0; j < n; j++) {
+pix1 = pixaaGetPix(recog->pixaa, i, j, L_CLONE);
+ptaaGetPt(recog->ptaa, i, j, &x1, &y1);  /* centroid */
+numaaGetValue(recog->naasum, i, j, NULL, &area1);  /* fg sum */
+nascore = numaCreate(n);
+for (k = 0; k < recog->setsize; k++) {  /* average templates */
+pix2 = pixaGetPix(recog->pixa, k, L_CLONE);
+ptaGetPt(recog->pta, k, &x2, &y2);  /* average centroid */
+numaGetIValue(recog->nasum, k, &area2);  /* average fg sum */
+pixCorrelationScoreSimple(pix1, pix2, area1, area2,
+x1 - x2, y1 - y2, 5, 5,
+recog->sumtab, &score);
+numaAddNumber(nascore, score);
+pixDestroy(&pix2);
+}
+/* Save templates that are in the correct class and
+* at or above threshold.  Toss any classes with less
+* than %minsize templates. */
+numaGetMax(nascore, &maxscore, &maxk);
+if (maxk == i && maxscore >= minscore && n >= minsize) {
+/* save it */
+pix3 = pixaaGetPix(recog->pixaa_u, i, j, L_COPY);
+pixaAddPix(pixad, pix3, L_INSERT);
+if (nasave) numaAddNumber(nasave, maxscore);
+} else if (ppixrem) {  /* outlier */
+pix3 = recogDisplayOutlier(recog, i, j, maxk, maxscore);
+pixaAddPix(pixarem, pix3, L_INSERT);
+}
+numaDestroy(&nascore);
+pixDestroy(&pix1);
+}
+}
+if (ppixsave) {
+*ppixsave = pixDisplayOutliers(pixad, nasave);
+numaDestroy(&nasave);
+}
+if (ppixrem) {
+*ppixrem = pixaDisplayTiledInRows(pixarem, 32, 1500, 1.0, 0, 20, 2);
+pixaDestroy(&pixarem);
+}
+numaDestroy(&nan);
+recogDestroy(&recog);
+return pixad;
+}
+/*------------------------------------------------------------------------*
+*                       Training on unlabeled data                       *
+*------------------------------------------------------------------------*/
+/*!
+* \brief   recogTrainFromBoot()
+*
+* \param[in]    recogboot   labeled boot recognizer
+* \param[in]    pixas       set of unlabeled input characters
+* \param[in]    minscore    min score for accepting the example; e.g., 0.75
+* \param[in]    threshold   for binarization, if needed
+* \param[in]    debug       1 for debug output saved to recogboot; 0 otherwise
+* \return  pixad   labeled version of input pixas, trained on a BSR,
+*                  or NULL on error
+*
+* <pre>
+* Notes:
+*      (1) This takes %pixas of unscaled single characters and %recboot,
+*          a bootstrep recognizer (BSR) that has been set up with parameters
+*            * scaleh: scale all templates to this height
+*            * linew: width of normalized strokes, or 0 if using
+*              the input image
+*          It modifies the pix in %pixas accordingly and correlates
+*          with the templates in the BSR.  It returns those input
+*          images in %pixas whose best correlation with the BSR is at
+*          or above %minscore.  The returned pix have added text labels
+*          for the text string of the class to which the best
+*          correlated template belongs.
+*      (2) Identification occurs in scaled mode (typically with h = 40),
+*          optionally using a width-normalized line images derived
+*          from those in %pixas.
+* </pre>
+*/
+PIXA  *
+recogTrainFromBoot(L_RECOG   *recogboot,
+PIXA      *pixas,
+l_float32  minscore,
+l_int32    threshold,
+l_int32    debug)
+{
+char      *text;
+l_int32    i, n, same, maxd, scaleh, linew;
+l_float32  score;
+PIX       *pix1, *pix2, *pixdb = NULL;
+PIXA      *pixa1, *pixa2, *pixa3, *pixad;
+if (!recogboot)
+return (PIXA *)ERROR_PTR("recogboot not defined", __func__, NULL);
+if (!pixas)
+return (PIXA *)ERROR_PTR("pixas not defined", __func__, NULL);
+/* Make sure all input pix are 1 bpp */
+if ((n = pixaGetCount(pixas)) == 0)
+return (PIXA *)ERROR_PTR("no pix in pixa", __func__, NULL);
+pixaVerifyDepth(pixas, &same, &maxd);
+if (maxd == 1) {
+pixa1 = pixaCopy(pixas, L_COPY);
+} else {
+pixa1 = pixaCreate(n);
+for (i = 0; i < n; i++) {
+pix1 = pixaGetPix(pixas, i, L_CLONE);
+pix2 = pixConvertTo1(pix1, threshold);
+pixaAddPix(pixa1, pix2, L_INSERT);
+pixDestroy(&pix1);
+}
+}
+/* Scale the input images to match the BSR */
+scaleh = recogboot->scaleh;
+linew = recogboot->linew;
+pixa2 = pixaCreate(n);
+for (i = 0; i < n; i++) {
+pix1 = pixaGetPix(pixa1, i, L_CLONE);
+pix2 = pixScaleToSize(pix1, 0, scaleh);
+pixaAddPix(pixa2, pix2, L_INSERT);
+pixDestroy(&pix1);
+}
+pixaDestroy(&pixa1);
+/* Optionally convert to width-normalized line */
+if (linew > 0)
+pixa3 = pixaSetStrokeWidth(pixa2, linew, 4, 8);
+else
+pixa3 = pixaCopy(pixa2, L_CLONE);
+pixaDestroy(&pixa2);
+/* Identify using recogboot */
+n = pixaGetCount(pixa3);
+pixad = pixaCreate(n);
+for (i = 0; i < n; i++) {
+pix1 = pixaGetPix(pixa3, i, L_COPY);
+pixSetText(pix1, NULL);  /* remove any existing text or labelling */
+if (!debug) {
+recogIdentifyPix(recogboot, pix1, NULL);
+} else {
+recogIdentifyPix(recogboot, pix1, &pixdb);
+pixaAddPix(recogboot->pixadb_boot, pixdb, L_INSERT);
+}
+rchExtract(recogboot->rch, NULL, &score, &text, NULL, NULL, NULL, NULL);
+if (score >= minscore) {
+pix2 = pixaGetPix(pixas, i, L_COPY);
+pixSetText(pix2, text);
+pixaAddPix(pixad, pix2, L_INSERT);
+pixaAddPix(recogboot->pixadb_boot, pixdb, L_COPY);
+}
+LEPT_FREE(text);
+pixDestroy(&pix1);
+}
+pixaDestroy(&pixa3);
+return pixad;
+}
+/*------------------------------------------------------------------------*
+*                     Padding the digit training set                     *
+*------------------------------------------------------------------------*/
+/*!
+* \brief   recogPadDigitTrainingSet()
+*
+* \param[in,out]   precog    trained; if padding is needed, it is replaced
+*                            by a a new padded recog
+* \param[in]       scaleh    must be > 0; suggest ~40.
+* \param[in]       linew     use 0 for original scanned images
+* \return       0 if OK, 1 on error
+*
+* <pre>
+* Notes:
+*      (1) This is a no-op if padding is not needed.  However,
+*          if it is, this replaces the input recog with a new recog,
+*          padded appropriately with templates from a boot recognizer,
+*          and set up with correlation templates derived from
+*          %scaleh and %linew.
+* </pre>
+*/
+l_ok
+recogPadDigitTrainingSet(L_RECOG  **precog,
+l_int32    scaleh,
+l_int32    linew)
+{
+PIXA     *pixa;
+L_RECOG  *recog1, *recog2;
+SARRAY   *sa;
+if (!precog)
+return ERROR_INT("&recog not defined", __func__, 1);
+recog1 = *precog;
+recogIsPaddingNeeded(recog1, &sa);
+if (!sa) return 0;
+/* Get a new pixa with the padding templates added */
+pixa = recogAddDigitPadTemplates(recog1, sa);
+sarrayDestroy(&sa);
+if (!pixa)
+return ERROR_INT("pixa not made", __func__, 1);
+/* Need to use templates that are scaled to a fixed height. */
+if (scaleh <= 0) {
+L_WARNING("templates must be scaled to fixed height; using %d\n",
+__func__, 40);
+scaleh = 40;
+}
+/* Create a hybrid recog, composed of templates from both
+* the original and bootstrap sources. */
+recog2 = recogCreateFromPixa(pixa, 0, scaleh, linew, recog1->threshold,
+recog1->maxyshift);
+pixaDestroy(&pixa);
+recogDestroy(precog);
+*precog = recog2;
+return 0;
+}
+/*!
+* \brief   recogIsPaddingNeeded()
+*
+* \param[in]    recog   trained
+* \param[out]   psa     addr of returned string containing text value
+* \return       1 on error; 0 if OK, whether or not additional padding
+*               templates are required.
+*
+* <pre>
+* Notes:
+*      (1) This returns a string array in &sa containing character values
+*          for which extra templates are needed; this sarray is
+*          used by recogGetPadTemplates().  It returns NULL
+*          if no padding templates are needed.
+* </pre>
+*/
+l_int32
+recogIsPaddingNeeded(L_RECOG  *recog,
+SARRAY  **psa)
+{
+char      *str;
+l_int32    i, nt, min_nopad, nclass, allclasses;
+l_float32  minval;
+NUMA      *naclass;
+SARRAY    *sa;
+if (!psa)
+return ERROR_INT("&sa not defined", __func__, 1);
+*psa = NULL;
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+/* Do we have samples from all classes? */
+nclass = pixaaGetCount(recog->pixaa_u, &naclass);  /* unscaled bitmaps */
+allclasses = (nclass == recog->charset_size) ? 1 : 0;
+/* Are there enough samples in each class already? */
+min_nopad = recog->min_nopad;
+numaGetMin(naclass, &minval, NULL);
+if (allclasses && (minval >= min_nopad)) {
+numaDestroy(&naclass);
+return 0;
+}
+/* Are any classes not represented? */
+sa = recogAddMissingClassStrings(recog);
+*psa = sa;
+/* Are any other classes under-represented? */
+for (i = 0; i < nclass; i++) {
+numaGetIValue(naclass, i, &nt);
+if (nt < min_nopad) {
+str = sarrayGetString(recog->sa_text, i, L_COPY);
+sarrayAddString(sa, str, L_INSERT);
+}
+}
+numaDestroy(&naclass);
+return 0;
+}
+/*!
+* \brief   recogAddMissingClassStrings()
+*
+* \param[in]    recog   trained
+* \return       sa  of class string missing in %recog, or NULL on error
+*
+* <pre>
+* Notes:
+*      (1) This returns an empty %sa if there is at least one template
+*          in each class in %recog.
+* </pre>
+*/
+static SARRAY  *
+recogAddMissingClassStrings(L_RECOG  *recog)
+{
+char    *text;
+char     str[4];
+l_int32  i, nclass, index, ival;
+NUMA    *na;
+SARRAY  *sa;
+if (!recog)
+return (SARRAY *)ERROR_PTR("recog not defined", __func__, NULL);
+/* Only handling digits */
+nclass = pixaaGetCount(recog->pixaa_u, NULL);  /* unscaled bitmaps */
+if (recog->charset_type != 1 || nclass == 10)
+return sarrayCreate(0);  /* empty */
+/* Make an indicator array for missing classes */
+na = numaCreate(0);
+sa = sarrayCreate(0);
+for (i = 0; i < recog->charset_size; i++)
+numaAddNumber(na, 1);
+for (i = 0; i < nclass; i++) {
+text = sarrayGetString(recog->sa_text, i, L_NOCOPY);
+index = text[0] - '0';
+numaSetValue(na, index, 0);
+}
+/* Convert to string and add to output */
+for (i = 0; i < nclass; i++) {
+numaGetIValue(na, i, &ival);
+if (ival == 1) {
+str[0] = '0' + i;
+str[1] = '\0';
+sarrayAddString(sa, str, L_COPY);
+}
+}
+numaDestroy(&na);
+return sa;
+}
+/*!
+* \brief   recogAddDigitPadTemplates()
+*
+* \param[in]    recog   trained
+* \param[in]    sa      set of text strings that need to be padded
+* \return  pixa   of all templates from %recog and the additional pad
+*                 templates from a boot recognizer; or NULL on error
+*
+* <pre>
+* Notes:
+*      (1) Call recogIsPaddingNeeded() first, which returns %sa of
+*          template text strings for classes where more templates
+*          are needed.
+* </pre>
+*/
+PIXA  *
+recogAddDigitPadTemplates(L_RECOG  *recog,
+SARRAY   *sa)
+{
+char    *str, *text;
+l_int32  i, j, n, nt;
+PIX     *pix;
+PIXA    *pixa1, *pixa2;
+if (!recog)
+return (PIXA *)ERROR_PTR("recog not defined", __func__, NULL);
+if (!sa)
+return (PIXA *)ERROR_PTR("sa not defined", __func__, NULL);
+if (recogCharsetAvailable(recog->charset_type) == FALSE)
+return (PIXA *)ERROR_PTR("boot charset not available", __func__, NULL);
+/* Make boot recog templates */
+pixa1 = recogMakeBootDigitTemplates(0, 0);
+n = pixaGetCount(pixa1);
+/* Extract the unscaled templates from %recog */
+pixa2 = recogExtractPixa(recog);
+/* Add selected boot recog templates based on the text strings in sa */
+nt = sarrayGetCount(sa);
+for (i = 0; i < n; i++) {
+pix = pixaGetPix(pixa1, i, L_CLONE);
+text = pixGetText(pix);
+for (j = 0; j < nt; j++) {
+str = sarrayGetString(sa, j, L_NOCOPY);
+if (!strcmp(text, str)) {
+pixaAddPix(pixa2, pix, L_COPY);
+break;
+}
+}
+pixDestroy(&pix);
+}
+pixaDestroy(&pixa1);
+return pixa2;
+}
+/*!
+* \brief   recogCharsetAvailable()
+*
+* \param[in]    type     of charset for padding
+* \return  1 if available; 0 if not.
+*/
+static l_int32
+recogCharsetAvailable(l_int32  type)
+{
+l_int32  ret;
+switch (type)
+{
+case L_ARABIC_NUMERALS:
+ret = TRUE;
+break;
+case L_LC_ROMAN_NUMERALS:
+case L_UC_ROMAN_NUMERALS:
+case L_LC_ALPHA:
+case L_UC_ALPHA:
+L_INFO("charset type %d not available\n", __func__, type);
+ret = FALSE;
+break;
+default:
+L_INFO("charset type %d is unknown\n", __func__, type);
+ret = FALSE;
+break;
+}
+return ret;
+}
+/*------------------------------------------------------------------------*
+*                      Making a boot digit recognizer                    *
+*------------------------------------------------------------------------*/
+/*!
+* \brief   recogMakeBootDigitRecog()
+*
+* \param[in]    nsamp       number of samples of each digit; or 0
+* \param[in]    scaleh      scale all heights to this; typ. use 40
+* \param[in]    linew       normalized line width; typ. use 5; 0 to skip
+* \param[in]    maxyshift   from nominal centroid alignment; typically 0 or 1
+* \param[in]    debug       1 for showing templates; 0 otherwise
+* \return  recog, or NULL on error
+*
+* <pre>
+* Notes:
+*     (1) This takes a set of pre-computed, labeled pixa of single
+*         digits, and generates a recognizer from them.
+*         The templates used in the recognizer can be modified by:
+*         - scaling (isotropically to fixed height)
+*         - generating a skeleton and thickening so that all strokes
+*           have the same width.
+*     (2) The resulting templates are scaled versions of either the
+*         input bitmaps or images with fixed line widths.  To use the
+*         input bitmaps, set %linew = 0; otherwise, set %linew to the
+*         desired line width.
+*     (3) If %nsamp == 0, this uses and extends the output from
+*         three boot generators:
+*            l_bootnum_gen1, l_bootnum_gen2, l_bootnum_gen3.
+*         Otherwise, it uses exactly %nsamp templates of each digit,
+*         extracted by l_bootnum_gen4.
+* </pre>
+*/
+L_RECOG  *
+recogMakeBootDigitRecog(l_int32  nsamp,
+l_int32  scaleh,
+l_int32  linew,
+l_int32  maxyshift,
+l_int32  debug)
+{
+PIXA     *pixa;
+L_RECOG  *recog;
+/* Get the templates, extended by horizontal scaling */
+pixa = recogMakeBootDigitTemplates(nsamp, debug);
+/* Make the boot recog; recogModifyTemplate() will scale the
+* templates and optionally turn them into strokes of fixed width. */
+recog = recogCreateFromPixa(pixa, 0, scaleh, linew, 128, maxyshift);
+pixaDestroy(&pixa);
+if (debug)
+recogShowContent(stderr, recog, 0, 1);
+return recog;
+}
+/*!
+* \brief   recogMakeBootDigitTemplates()
+*
+* \param[in]    nsamp     number of samples of each digit; or 0
+* \param[in]    debug     1 for display of templates
+* \return  pixa   of templates; or NULL on error
+*
+* <pre>
+* Notes:
+*     (1) See recogMakeBootDigitRecog().
+* </pre>
+*/
+PIXA  *
+recogMakeBootDigitTemplates(l_int32  nsamp,
+l_int32  debug)
+{
+NUMA  *na1;
+PIX   *pix1, *pix2, *pix3;
+PIXA  *pixa1, *pixa2, *pixa3;
+if (nsamp > 0) {
+pixa1 = l_bootnum_gen4(nsamp);
+if (debug) {
+pix1 = pixaDisplayTiledWithText(pixa1, 1500, 1.0, 10,
+2, 6, 0xff000000);
+pixDisplay(pix1, 0, 0);
+pixDestroy(&pix1);
+}
+return pixa1;
+}
+/* Else, generate from 3 pixa */
+pixa1 = l_bootnum_gen1();
+pixa2 = l_bootnum_gen2();
+pixa3 = l_bootnum_gen3();
+if (debug) {
+pix1 = pixaDisplayTiledWithText(pixa1, 1500, 1.0, 10, 2, 6, 0xff000000);
+pix2 = pixaDisplayTiledWithText(pixa2, 1500, 1.0, 10, 2, 6, 0xff000000);
+pix3 = pixaDisplayTiledWithText(pixa3, 1500, 1.0, 10, 2, 6, 0xff000000);
+pixDisplay(pix1, 0, 0);
+pixDisplay(pix2, 600, 0);
+pixDisplay(pix3, 1200, 0);
+pixDestroy(&pix1);
+pixDestroy(&pix2);
+pixDestroy(&pix3);
+}
+pixaJoin(pixa1, pixa2, 0, -1);
+pixaJoin(pixa1, pixa3, 0, -1);
+pixaDestroy(&pixa2);
+pixaDestroy(&pixa3);
+/* Extend by horizontal scaling */
+na1 = numaCreate(4);
+numaAddNumber(na1, 0.9f);
+numaAddNumber(na1, 1.1f);
+numaAddNumber(na1, 1.2f);
+pixa2 = pixaExtendByScaling(pixa1, na1, L_HORIZ, 1);
+pixaDestroy(&pixa1);
+numaDestroy(&na1);
+return pixa2;
+}
+/*------------------------------------------------------------------------*
+*                               Debugging                                *
+*------------------------------------------------------------------------*/
+/*!
+* \brief   recogShowContent()
+*
+* \param[in]    fp       file stream
+* \param[in]    recog
+* \param[in]    index    for naming of output files of template images
+* \param[in]    display  1 for showing template images; 0 otherwise
+* \return  0 if OK, 1 on error
+*/
+l_ok
+recogShowContent(FILE     *fp,
+L_RECOG  *recog,
+l_int32   index,
+l_int32   display)
+{
+char     buf[128];
+l_int32  i, val, count;
+PIX     *pix;
+NUMA    *na;
+if (!fp)
+return ERROR_INT("stream not defined", __func__, 1);
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+fprintf(fp, "Debug print of recog contents\n");
+fprintf(fp, "  Setsize: %d\n", recog->setsize);
+fprintf(fp, "  Binarization threshold: %d\n", recog->threshold);
+fprintf(fp, "  Maximum matching y-jiggle: %d\n", recog->maxyshift);
+if (recog->linew <= 0)
+fprintf(fp, "  Using image templates for matching\n");
+else
+fprintf(fp, "  Using templates with fixed line width for matching\n");
+if (recog->scalew == 0)
+fprintf(fp, "  No width scaling of templates\n");
+else
+fprintf(fp, "  Template width scaled to %d\n", recog->scalew);
+if (recog->scaleh == 0)
+fprintf(fp, "  No height scaling of templates\n");
+else
+fprintf(fp, "  Template height scaled to %d\n", recog->scaleh);
+fprintf(fp, "  Number of samples in each class:\n");
+pixaaGetCount(recog->pixaa_u, &na);
+for (i = 0; i < recog->setsize; i++) {
+l_dnaGetIValue(recog->dna_tochar, i, &val);
+numaGetIValue(na, i, &count);
+if (val < 128)
+fprintf(fp, "    class %d, char %c:   %d\n", i, val, count);
+else
+fprintf(fp, "    class %d, val %d:   %d\n", i, val, count);
+}
+numaDestroy(&na);
+if (display) {
+lept_mkdir("lept/recog");
+pix = pixaaDisplayByPixa(recog->pixaa_u, 50, 1.0, 20, 20, 0);
+snprintf(buf, sizeof(buf), "/tmp/lept/recog/templates_u.%d.png", index);
+pixWriteDebug(buf, pix, IFF_PNG);
+pixDisplay(pix, 0, 200 * index);
+pixDestroy(&pix);
+if (recog->train_done) {
+pix = pixaaDisplayByPixa(recog->pixaa, 50, 1.0, 20, 20, 0);
+snprintf(buf, sizeof(buf),
+"/tmp/lept/recog/templates.%d.png", index);
+pixWriteDebug(buf, pix, IFF_PNG);
+pixDisplay(pix, 800, 200 * index);
+pixDestroy(&pix);
+}
+}
+return 0;
+}
+/*!
+* \brief   recogDebugAverages()
+*
+* \param[in]    recog    addr of recog
+* \param[in]    debug     0 no output; 1 for images; 2 for text; 3 for both
+* \return  0 if OK, 1 on error
+*
+* <pre>
+* Notes:
+*      (1) Generates an image that pairs each of the input images used
+*          in training with the average template that it is best
+*          correlated to.  This is written into the recog.
+*      (2) It also generates pixa_tr of all the input training images,
+*          which can be used, e.g., in recogShowMatchesInRange().
+*      (3) Returns an error if the averaging function finds bad classes.
+* </pre>
+*/
+l_ok
+recogDebugAverages(L_RECOG  *recog,
+l_int32   debug)
+{
+l_int32    i, j, n, np, index;
+l_float32  score;
+PIX       *pix1, *pix2, *pix3;
+PIXA      *pixa, *pixat;
+PIXAA     *paa1, *paa2;
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+/* Mark the training as finished if necessary, and make sure
+* that the average templates have been built. */
+if (recogAverageSamples(recog, 0) != 0)
+return ERROR_INT("averaging failed", __func__, 1);
+/* Save a pixa of all the training examples */
+paa1 = recog->pixaa;
+if (!recog->pixa_tr)
+recog->pixa_tr = pixaaFlattenToPixa(paa1, NULL, L_CLONE);
+/* Destroy any existing image and make a new one */
+if (recog->pixdb_ave)
+pixDestroy(&recog->pixdb_ave);
+n = pixaaGetCount(paa1, NULL);
+paa2 = pixaaCreate(n);
+for (i = 0; i < n; i++) {
+pixa = pixaCreate(0);
+pixat = pixaaGetPixa(paa1, i, L_CLONE);
+np = pixaGetCount(pixat);
+for (j = 0; j < np; j++) {
+pix1 = pixaaGetPix(paa1, i, j, L_CLONE);
+recogIdentifyPix(recog, pix1, &pix2);
+rchExtract(recog->rch, &index, &score, NULL, NULL, NULL,
+NULL, NULL);
+if (debug >= 2)
+lept_stderr("index = %d, score = %7.3f\n", index, score);
+pix3 = pixAddBorder(pix2, 2, 1);
+pixaAddPix(pixa, pix3, L_INSERT);
+pixDestroy(&pix1);
+pixDestroy(&pix2);
+}
+pixaaAddPixa(paa2, pixa, L_INSERT);
+pixaDestroy(&pixat);
+}
+recog->pixdb_ave = pixaaDisplayByPixa(paa2, 50, 1.0, 20, 20, 0);
+if (debug % 2) {
+lept_mkdir("lept/recog");
+pixWriteDebug("/tmp/lept/recog/templ_match.png", recog->pixdb_ave,
+IFF_PNG);
+pixDisplay(recog->pixdb_ave, 100, 100);
+}
+pixaaDestroy(&paa2);
+return 0;
+}
+/*!
+* \brief   recogShowAverageTemplates()
+*
+* \param[in]    recog
+* \return  0 on success, 1 on failure
+*
+* <pre>
+* Notes:
+*      (1) This debug routine generates a display of the averaged templates,
+*          both scaled and unscaled, with the centroid visible in red.
+* </pre>
+*/
+l_int32
+recogShowAverageTemplates(L_RECOG  *recog)
+{
+l_int32    i, size;
+l_float32  x, y;
+PIX       *pix1, *pix2, *pixr;
+PIXA      *pixat, *pixadb;
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+lept_stderr("min/max width_u = (%d,%d); min/max height_u = (%d,%d)\n",
+recog->minwidth_u, recog->maxwidth_u,
+recog->minheight_u, recog->maxheight_u);
+lept_stderr("min splitw = %d, max splith = %d\n",
+recog->min_splitw, recog->max_splith);
+pixaDestroy(&recog->pixadb_ave);
+pixr = pixCreate(3, 3, 32);  /* 3x3 red square for centroid location */
+pixSetAllArbitrary(pixr, 0xff000000);
+pixadb = pixaCreate(2);
+/* Unscaled bitmaps */
+size = recog->setsize;
+pixat = pixaCreate(size);
+for (i = 0; i < size; i++) {
+if ((pix1 = pixaGetPix(recog->pixa_u, i, L_CLONE)) == NULL)
+continue;
+pix2 = pixConvertTo32(pix1);
+ptaGetPt(recog->pta_u, i, &x, &y);
+pixRasterop(pix2, (l_int32)(x - 0.5), (l_int32)(y - 0.5), 3, 3,
+PIX_SRC, pixr, 0, 0);
+pixaAddPix(pixat, pix2, L_INSERT);
+pixDestroy(&pix1);
+}
+pix1 = pixaDisplayTiledInRows(pixat, 32, 3000, 1.0, 0, 20, 0);
+pixaAddPix(pixadb, pix1, L_INSERT);
+pixDisplay(pix1, 100, 100);
+pixaDestroy(&pixat);
+/* Scaled bitmaps */
+pixat = pixaCreate(size);
+for (i = 0; i < size; i++) {
+if ((pix1 = pixaGetPix(recog->pixa, i, L_CLONE)) == NULL)
+continue;
+pix2 = pixConvertTo32(pix1);
+ptaGetPt(recog->pta, i, &x, &y);
+pixRasterop(pix2, (l_int32)(x - 0.5), (l_int32)(y - 0.5), 3, 3,
+PIX_SRC, pixr, 0, 0);
+pixaAddPix(pixat, pix2, L_INSERT);
+pixDestroy(&pix1);
+}
+pix1 = pixaDisplayTiledInRows(pixat, 32, 3000, 1.0, 0, 20, 0);
+pixaAddPix(pixadb, pix1, L_INSERT);
+pixDisplay(pix1, 100, 100);
+pixaDestroy(&pixat);
+pixDestroy(&pixr);
+recog->pixadb_ave = pixadb;
+return 0;
+}
+/*!
+* \brief   pixDisplayOutliers()
+*
+* \param[in]    pixas    unscaled labeled templates
+* \param[in]    nas      scores of templates (against class averages)
+* \return  pix    tiled pixa with text and scores, or NULL on failure
+*
+* <pre>
+* Notes:
+*      (1) This debug routine is called from recogRemoveOutliers2(),
+*          and takes the saved templates and their scores as input.
+* </pre>
+*/
+static PIX  *
+pixDisplayOutliers(PIXA  *pixas,
+NUMA  *nas)
+{
+char      *text;
+char       buf[16];
+l_int32    i, n;
+l_float32  fval;
+PIX       *pix1, *pix2;
+PIXA      *pixa1;
+if (!pixas)
+return (PIX *)ERROR_PTR("pixas not defined", __func__, NULL);
+if (!nas)
+return (PIX *)ERROR_PTR("nas not defined", __func__, NULL);
+n = pixaGetCount(pixas);
+if (numaGetCount(nas) != n)
+return (PIX *)ERROR_PTR("pixas and nas sizes differ", __func__, NULL);
+pixa1 = pixaCreate(n);
+for (i = 0; i < n; i++) {
+pix1 = pixaGetPix(pixas, i, L_CLONE);
+pix2 = pixAddBlackOrWhiteBorder(pix1, 25, 25, 0, 0, L_GET_WHITE_VAL);
+text = pixGetText(pix1);
+numaGetFValue(nas, i, &fval);
+snprintf(buf, sizeof(buf), "'%s': %5.2f", text, fval);
+pixSetText(pix2, buf);
+pixaAddPix(pixa1, pix2, L_INSERT);
+pixDestroy(&pix1);
+}
+pix1 = pixaDisplayTiledWithText(pixa1, 1500, 1.0, 20, 2, 6, 0xff000000);
+pixaDestroy(&pixa1);
+return pix1;
+}
+/*!
+* \brief   recogDisplayOutlier()
+*
+* \param[in]    recog
+* \param[in]    iclass     sample is in this class
+* \param[in]    jsamp      index of sample is class i
+* \param[in]    maxclass   index of class with closest average to sample
+* \param[in]    maxscore   score of sample with average of class %maxclass
+* \return  pix  sample and template images, with score, or NULL on error
+*
+* <pre>
+* Notes:
+*      (1) This shows three templates, side-by-side:
+*          - The outlier sample
+*          - The average template from the same class
+*          - The average class template that best matched the outlier sample
+* </pre>
+*/
+static PIX  *
+recogDisplayOutlier(L_RECOG   *recog,
+l_int32    iclass,
+l_int32    jsamp,
+l_int32    maxclass,
+l_float32  maxscore)
+{
+char   buf[64];
+PIX   *pix1, *pix2, *pix3, *pix4, *pix5;
+PIXA  *pixa;
+if (!recog)
+return (PIX *)ERROR_PTR("recog not defined", __func__, NULL);
+pix1 = pixaaGetPix(recog->pixaa, iclass, jsamp, L_CLONE);
+pix2 = pixaGetPix(recog->pixa, iclass, L_CLONE);
+pix3 = pixaGetPix(recog->pixa, maxclass, L_CLONE);
+pixa = pixaCreate(3);
+pixaAddPix(pixa, pix1, L_INSERT);
+pixaAddPix(pixa, pix2, L_INSERT);
+pixaAddPix(pixa, pix3, L_INSERT);
+pix4 = pixaDisplayTiledInRows(pixa, 32, 400, 2.0, 0, 12, 2);
+snprintf(buf, sizeof(buf), "C=%d, BAC=%d, S=%4.2f", iclass, maxclass,
+maxscore);
+pix5 = pixAddSingleTextblock(pix4, recog->bmf, buf, 0xff000000,
+L_ADD_BELOW, NULL);
+pixDestroy(&pix4);
+pixaDestroy(&pixa);
+return pix5;
+}
+/*!
+* \brief   recogShowMatchesInRange()
+*
+* \param[in]    recog
+* \param[in]    pixa        of 1 bpp images to match
+* \param[in]    minscore    min score to include output
+* \param[in]    maxscore    max score to include output
+* \param[in]    display     1 to display the result
+* \return  0 if OK, 1 on error
+*
+* <pre>
+* Notes:
+*      (1) This gives a visual output of the best matches for a given
+*          range of scores.  Each pair of images can optionally be
+*          labeled with the index of the best match and the correlation.
+*      (2) To use this, save a set of 1 bpp images (labeled or
+*          unlabeled) that can be given to a recognizer in a pixa.
+*          Then call this function with the pixa and parameters
+*          to filter a range of scores.
+* </pre>
+*/
+l_ok
+recogShowMatchesInRange(L_RECOG   *recog,
+PIXA      *pixa,
+l_float32  minscore,
+l_float32  maxscore,
+l_int32    display)
+{
+l_int32    i, n, index, depth;
+l_float32  score;
+NUMA      *nascore, *naindex;
+PIX       *pix1, *pix2;
+PIXA      *pixa1, *pixa2;
+if (!recog)
+return ERROR_INT("recog not defined", __func__, 1);
+if (!pixa)
+return ERROR_INT("pixa not defined", __func__, 1);
+/* Run the recognizer on the set of images */
+n = pixaGetCount(pixa);
+nascore = numaCreate(n);
+naindex = numaCreate(n);
+pixa1 = pixaCreate(n);
+for (i = 0; i < n; i++) {
+pix1 = pixaGetPix(pixa, i, L_CLONE);
+recogIdentifyPix(recog, pix1, &pix2);
+rchExtract(recog->rch, &index, &score, NULL, NULL, NULL, NULL, NULL);
+numaAddNumber(nascore, score);
+numaAddNumber(naindex, index);
+pixaAddPix(pixa1, pix2, L_INSERT);
+pixDestroy(&pix1);
+}
+/* Filter the set and optionally add text to each */
+pixa2 = pixaCreate(n);
+depth = 1;
+for (i = 0; i < n; i++) {
+numaGetFValue(nascore, i, &score);
+if (score < minscore || score > maxscore) continue;
+pix1 = pixaGetPix(pixa1, i, L_CLONE);
+numaGetIValue(naindex, i, &index);
+pix2 = recogShowMatch(recog, pix1, NULL, NULL, index, score);
+if (i == 0) depth = pixGetDepth(pix2);
+pixaAddPix(pixa2, pix2, L_INSERT);
+pixDestroy(&pix1);
+}
+/* Package it up */
+pixDestroy(&recog->pixdb_range);
+if (pixaGetCount(pixa2) > 0) {
+recog->pixdb_range =
+pixaDisplayTiledInRows(pixa2, depth, 2500, 1.0, 0, 20, 1);
+if (display)
+pixDisplay(recog->pixdb_range, 300, 100);
+} else {
+L_INFO("no character matches in the range of scores\n", __func__);
+}
+pixaDestroy(&pixa1);
+pixaDestroy(&pixa2);
+numaDestroy(&nascore);
+numaDestroy(&naindex);
+return 0;
+}
+/*!
+* \brief   recogShowMatch()
+*
+* \param[in]    recog
+* \param[in]    pix1    input pix; several possibilities
+* \param[in]    pix2    [optional] matching template
+* \param[in]    box     [optional] region in pix1 for which pix2 matches
+* \param[in]    index   index of matching template; use -1 to disable printing
+* \param[in]    score   score of match
+* \return  pixd pair of images, showing input pix and best template,
+*                    optionally with matching information, or NULL on error.
+*
+* <pre>
+* Notes:
+*      (1) pix1 can be one of these:
+*          (a) The input pix alone, which can be either a single character
+*              (box == NULL) or several characters that need to be
+*              segmented.  If more than character is present, the box
+*              region is displayed with an outline.
+*          (b) Both the input pix and the matching template.  In this case,
+*              pix2 and box will both be null.
+*      (2) If the bmf has been made (by a call to recogMakeBmf())
+*          and the index >= 0, the text field, match score and index
+*          will be rendered; otherwise their values will be ignored.
+* </pre>
+*/
+PIX *
+recogShowMatch(L_RECOG   *recog,
+PIX       *pix1,
+PIX       *pix2,
+BOX       *box,
+l_int32    index,
+l_float32  score)
+{
+char    buf[32];
+char   *text;
+L_BMF  *bmf;
+PIX    *pix3, *pix4, *pix5, *pixd;
+PIXA   *pixa;
+if (!recog)
+return (PIX *)ERROR_PTR("recog not defined", __func__, NULL);
+if (!pix1)
+return (PIX *)ERROR_PTR("pix1 not defined", __func__, NULL);
+bmf = (recog->bmf && index >= 0) ? recog->bmf : NULL;
+if (!pix2 && !box && !bmf)  /* nothing to do */
+return pixCopy(NULL, pix1);
+pix3 = pixConvertTo32(pix1);
+if (box)
+pixRenderBoxArb(pix3, box, 1, 255, 0, 0);
+if (pix2) {
+pixa = pixaCreate(2);
+pixaAddPix(pixa, pix3, L_CLONE);
+pixaAddPix(pixa, pix2, L_CLONE);
+pix4 = pixaDisplayTiledInRows(pixa, 1, 500, 1.0, 0, 15, 0);
+pixaDestroy(&pixa);
+} else {
+pix4 = pixCopy(NULL, pix3);
+}
+pixDestroy(&pix3);
+if (bmf) {
+pix5 = pixAddBorderGeneral(pix4, 55, 55, 0, 0, 0xffffff00);
+recogGetClassString(recog, index, &text);
+snprintf(buf, sizeof(buf), "C=%s, S=%4.3f, I=%d", text, score, index);
+pixd = pixAddSingleTextblock(pix5, bmf, buf, 0xff000000,
+L_ADD_BELOW, NULL);
+pixDestroy(&pix5);
+LEPT_FREE(text);
+} else {
+pixd = pixClone(pix4);
+}
+pixDestroy(&pix4);
+return pixd;
+}

Mercurial > hgrepos > Python2 > PyMuPDF

comparison mupdf-source/thirdparty/leptonica/src/recogtrain.c @ 2:b50eed0cc0ef upstream