--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/tesseract/src/lstm/fullyconnected.h	Mon Sep 15 11:43:07 2025 +0200
@@ -0,0 +1,132 @@
+///////////////////////////////////////////////////////////////////////
+// File:        fullyconnected.h
+// Description: Simple feed-forward layer with various non-linearities.
+// Author:      Ray Smith
+//
+// (C) Copyright 2014, Google Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// http://www.apache.org/licenses/LICENSE-2.0
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+///////////////////////////////////////////////////////////////////////
+
+#ifndef TESSERACT_LSTM_FULLYCONNECTED_H_
+#define TESSERACT_LSTM_FULLYCONNECTED_H_
+
+#include "network.h"
+#include "networkscratch.h"
+#include "tesstypes.h"
+
+namespace tesseract {
+
+// C++ Implementation of the Softmax (output) class from lstm.py.
+class FullyConnected : public Network {
+public:
+  TESS_API
+  FullyConnected(const std::string &name, int ni, int no, NetworkType type);
+  ~FullyConnected() override = default;
+
+  // Returns the shape output from the network given an input shape (which may
+  // be partially unknown ie zero).
+  StaticShape OutputShape(const StaticShape &input_shape) const override;
+
+  std::string spec() const override {
+    std::string spec;
+    if (type_ == NT_TANH) {
+      spec += "Ft" + std::to_string(no_);
+    } else if (type_ == NT_LOGISTIC) {
+      spec += "Fs" + std::to_string(no_);
+    } else if (type_ == NT_RELU) {
+      spec += "Fr" + std::to_string(no_);
+    } else if (type_ == NT_LINEAR) {
+      spec += "Fl" + std::to_string(no_);
+    } else if (type_ == NT_POSCLIP) {
+      spec += "Fp" + std::to_string(no_);
+    } else if (type_ == NT_SYMCLIP) {
+      spec += "Fn" + std::to_string(no_);
+    } else if (type_ == NT_SOFTMAX) {
+      spec += "Fc" + std::to_string(no_);
+    } else {
+      spec += "Fm" + std::to_string(no_);
+    }
+    return spec;
+  }
+
+  // Changes the type to the given type. Used to commute a softmax to a
+  // non-output type for adding on other networks.
+  void ChangeType(NetworkType type) {
+    type_ = type;
+  }
+
+  // Suspends/Enables training by setting the training_ flag. Serialize and
+  // DeSerialize only operate on the run-time data if state is false.
+  void SetEnableTraining(TrainingState state) override;
+
+  // Sets up the network for training. Initializes weights using weights of
+  // scale `range` picked according to the random number generator `randomizer`.
+  int InitWeights(float range, TRand *randomizer) override;
+  // Recursively searches the network for softmaxes with old_no outputs,
+  // and remaps their outputs according to code_map. See network.h for details.
+  int RemapOutputs(int old_no, const std::vector<int> &code_map) override;
+
+  // Converts a float network to an int network.
+  void ConvertToInt() override;
+
+  // Provides debug output on the weights.
+  void DebugWeights() override;
+
+  // Writes to the given file. Returns false in case of error.
+  bool Serialize(TFile *fp) const override;
+  // Reads from the given file. Returns false in case of error.
+  bool DeSerialize(TFile *fp) override;
+
+  // Runs forward propagation of activations on the input line.
+  // See Network for a detailed discussion of the arguments.
+  void Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
+               NetworkScratch *scratch, NetworkIO *output) override;
+  // Components of Forward so FullyConnected can be reused inside LSTM.
+  void SetupForward(const NetworkIO &input, const TransposedArray *input_transpose);
+  void ForwardTimeStep(int t, TFloat *output_line);
+  void ForwardTimeStep(const TFloat *d_input, int t, TFloat *output_line);
+  void ForwardTimeStep(const int8_t *i_input, int t, TFloat *output_line);
+
+  // Runs backward propagation of errors on the deltas line.
+  // See Network for a detailed discussion of the arguments.
+  bool Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,
+                NetworkIO *back_deltas) override;
+  // Components of Backward so FullyConnected can be reused inside LSTM.
+  void BackwardTimeStep(const NetworkIO &fwd_deltas, int t, TFloat *curr_errors,
+                        TransposedArray *errors_t, TFloat *backprop);
+  void FinishBackward(const TransposedArray &errors_t);
+
+  // Updates the weights using the given learning rate, momentum and adam_beta.
+  // num_samples is used in the adam computation iff use_adam_ is true.
+  void Update(float learning_rate, float momentum, float adam_beta, int num_samples) override;
+  // Sums the products of weight updates in *this and other, splitting into
+  // positive (same direction) in *same and negative (different direction) in
+  // *changed.
+  void CountAlternators(const Network &other, TFloat *same, TFloat *changed) const override;
+
+protected:
+  // Weight arrays of size [no, ni + 1].
+  WeightMatrix weights_;
+  // Transposed copy of input used during training of size [ni, width].
+  TransposedArray source_t_;
+  // Pointer to transposed input stored elsewhere. If not null, this is used
+  // in preference to calculating the transpose and storing it in source_t_.
+  const TransposedArray *external_source_;
+  // Activations from forward pass of size [width, no].
+  NetworkIO acts_;
+  // Memory of the integer mode input to forward as softmax always outputs
+  // float, so the information is otherwise lost.
+  bool int_mode_;
+};
+
+} // namespace tesseract.
+
+#endif // TESSERACT_LSTM_FULLYCONNECTED_H_