Fixes to training process to allow incremental training from a recogn…

…ition model

ccmain/linerec.cpp

@@ -64,6 +64,7 @@ void Tesseract::TrainLineRecognizer(const STRING& input_imagename,
     return;
   }
   TrainFromBoxes(boxes, texts, block_list, &images);
+  images.Shuffle();
   if (!images.SaveDocument(lstmf_name.string(), NULL)) {
     tprintf("Failed to write training data to %s!\n", lstmf_name.string());
   }
@@ -79,7 +80,10 @@ void Tesseract::TrainFromBoxes(const GenericVector<TBOX>& boxes,
   int box_count = boxes.size();
   // Process all the text lines in this page, as defined by the boxes.
   int end_box = 0;
-  for (int start_box = 0; start_box < box_count; start_box = end_box) {
+  // Don't let \t, which marks newlines in the box file, get into the line
+  // content, as that makes the line unusable in training.
+  while (end_box < texts.size() && texts[end_box] == "\t") ++end_box;
+  for (int start_box = end_box; start_box < box_count; start_box = end_box) {
     // Find the textline of boxes starting at start and their bounding box.
     TBOX line_box = boxes[start_box];
     STRING line_str = texts[start_box];
@@ -115,7 +119,9 @@ void Tesseract::TrainFromBoxes(const GenericVector<TBOX>& boxes,
     }
     if (imagedata != NULL)
       training_data->AddPageToDocument(imagedata);
-    if (end_box < texts.size() && texts[end_box] == "\t") ++end_box;
+    // Don't let \t, which marks newlines in the box file, get into the line
+    // content, as that makes the line unusable in training.
+    while (end_box < texts.size() && texts[end_box] == "\t") ++end_box;
   }
 }
 

ccstruct/boxread.cpp

@@ -55,6 +55,8 @@ bool ReadAllBoxes(int target_page, bool skip_blanks, const STRING& filename,
   GenericVector<char> box_data;
   if (!tesseract::LoadDataFromFile(BoxFileName(filename), &box_data))
     return false;
+  // Convert the array of bytes to a string, so it can be used by the parser.
+  box_data.push_back('\0');
   return ReadMemBoxes(target_page, skip_blanks, &box_data[0], boxes, texts,
                       box_texts, pages);
 }

ccstruct/imagedata.cpp

@@ -24,18 +24,18 @@
 
 #include "imagedata.h"
 
+#if defined(__MINGW32__)
+#include <unistd.h>
+#else
+#include <thread>
+#endif
+
 #include "allheaders.h"
 #include "boxread.h"
 #include "callcpp.h"
 #include "helpers.h"
 #include "tprintf.h"
 
-#if defined(__MINGW32__)
-# include <unistd.h>
-#else
-# include <thread>
-#endif
-
 // Number of documents to read ahead while training. Doesn't need to be very
 // large.
 const int kMaxReadAhead = 8;
@@ -496,6 +496,21 @@ inT64 DocumentData::UnCache() {
   return memory_saved;
 }
 
+// Shuffles all the pages in the document.
+void DocumentData::Shuffle() {
+  TRand random;
+  // Different documents get shuffled differently, but the same for the same
+  // name.
+  random.set_seed(document_name_.string());
+  int num_pages = pages_.size();
+  // Execute one random swap for each page in the document.
+  for (int i = 0; i < num_pages; ++i) {
+    int src = random.IntRand() % num_pages;
+    int dest = random.IntRand() % num_pages;
+    std::swap(pages_[src], pages_[dest]);
+  }
+}
+
 // Locks the pages_mutex_ and Loads as many pages can fit in max_memory_
 // starting at index pages_offset_.
 bool DocumentData::ReCachePages() {

ccstruct/imagedata.h

@@ -266,6 +266,8 @@ class DocumentData {
   // Removes all pages from memory and frees the memory, but does not forget
   // the document metadata. Returns the memory saved.
   inT64 UnCache();
+  // Shuffles all the pages in the document.
+  void Shuffle();
 
  private:
   // Sets the value of total_pages_ behind a mutex.

ccstruct/pageres.cpp

@@ -529,13 +529,12 @@ void WERD_RES::FilterWordChoices(int debug_level) {
       if (choice->unichar_id(i) != best_choice->unichar_id(j) &&
           choice->certainty(i) - best_choice->certainty(j) < threshold) {
         if (debug_level >= 2) {
-          STRING label;
-          label.add_str_int("\nDiscarding bad choice #", index);
-          choice->print(label.string());
-          tprintf("i %d j %d Chunk %d Choice->Blob[i].Certainty %.4g"
-              " BestChoice->ChunkCertainty[Chunk] %g Threshold %g\n",
-              i, j, chunk, choice->certainty(i),
-              best_choice->certainty(j), threshold);
+          choice->print("WorstCertaintyDiffWorseThan");
+          tprintf(
+              "i %d j %d Choice->Blob[i].Certainty %.4g"
+              " WorstOtherChoiceCertainty %g Threshold %g\n",
+              i, j, choice->certainty(i), best_choice->certainty(j), threshold);
+          tprintf("Discarding bad choice #%d\n", index);
         }
         delete it.extract();
         break;

ccutil/genericvector.h

@@ -363,8 +363,7 @@ inline bool LoadDataFromFile(const STRING& filename,
   fseek(fp, 0, SEEK_END);
   size_t size = ftell(fp);
   fseek(fp, 0, SEEK_SET);
-  // Pad with a 0, just in case we treat the result as a string.
-  data->init_to_size(static_cast<int>(size) + 1, 0);
+  data->init_to_size(static_cast<int>(size), 0);
   bool result = fread(&(*data)[0], 1, size, fp) == size;
   fclose(fp);
   return result;
@@ -380,6 +379,17 @@ inline bool SaveDataToFile(const GenericVector<char>& data,
   fclose(fp);
   return result;
 }
+// Reads a file as a vector of STRING.
+inline bool LoadFileLinesToStrings(const STRING& filename,
+                                   GenericVector<STRING>* lines) {
+  GenericVector<char> data;
+  if (!LoadDataFromFile(filename.string(), &data)) {
+    return false;
+  }
+  STRING lines_str(&data[0], data.size());
+  lines_str.split('\n', lines);
+  return true;
+}
 
 template <typename T>
 bool cmp_eq(T const & t1, T const & t2) {

ccutil/helpers.h

@@ -27,6 +27,8 @@
 
 #include <stdio.h>
 #include <string.h>
+#include <functional>
+#include <string>
 
 #include "host.h"
 
@@ -43,6 +45,11 @@ class TRand {
   void set_seed(uinT64 seed) {
     seed_ = seed;
   }
+  // Sets the seed using a hash of a string.
+  void set_seed(const std::string& str) {
+    std::hash<std::string> hasher;
+    set_seed(static_cast<uinT64>(hasher(str)));
+  }
 
   // Returns an integer in the range 0 to MAX_INT32.
   inT32 IntRand() {

lstm/fullyconnected.cpp

@@ -56,6 +56,17 @@ StaticShape FullyConnected::OutputShape(const StaticShape& input_shape) const {
   return result;
 }
 
+// Suspends/Enables training by setting the training_ flag. Serialize and
+// DeSerialize only operate on the run-time data if state is false.
+void FullyConnected::SetEnableTraining(TrainingState state) {
+  if (state == TS_RE_ENABLE) {
+    if (training_ == TS_DISABLED) weights_.InitBackward(false);
+    training_ = TS_ENABLED;
+  } else {
+    training_ = state;
+  }
+}
+
 // Sets up the network for training. Initializes weights using weights of
 // scale `range` picked according to the random number generator `randomizer`.
 int FullyConnected::InitWeights(float range, TRand* randomizer) {
@@ -78,14 +89,14 @@ void FullyConnected::DebugWeights() {
 // Writes to the given file. Returns false in case of error.
 bool FullyConnected::Serialize(TFile* fp) const {
   if (!Network::Serialize(fp)) return false;
-  if (!weights_.Serialize(training_, fp)) return false;
+  if (!weights_.Serialize(IsTraining(), fp)) return false;
   return true;
 }
 
 // Reads from the given file. Returns false in case of error.
 // If swap is true, assumes a big/little-endian swap is needed.
 bool FullyConnected::DeSerialize(bool swap, TFile* fp) {
-  if (!weights_.DeSerialize(training_, swap, fp)) return false;
+  if (!weights_.DeSerialize(IsTraining(), swap, fp)) return false;
   return true;
 }
 
@@ -129,14 +140,14 @@ void FullyConnected::Forward(bool debug, const NetworkIO& input,
     }
     ForwardTimeStep(d_input, i_input, t, temp_line);
     output->WriteTimeStep(t, temp_line);
-    if (training() && type_ != NT_SOFTMAX) {
+    if (IsTraining() && type_ != NT_SOFTMAX) {
       acts_.CopyTimeStepFrom(t, *output, t);
     }
   }
   // Zero all the elements that are in the padding around images that allows
   // multiple different-sized images to exist in a single array.
   // acts_ is only used if this is not a softmax op.
-  if (training() && type_ != NT_SOFTMAX) {
+  if (IsTraining() && type_ != NT_SOFTMAX) {
     acts_.ZeroInvalidElements();
   }
   output->ZeroInvalidElements();
@@ -152,7 +163,7 @@ void FullyConnected::SetupForward(const NetworkIO& input,
                                   const TransposedArray* input_transpose) {
   // Softmax output is always float, so save the input type.
   int_mode_ = input.int_mode();
-  if (training()) {
+  if (IsTraining()) {
     acts_.Resize(input, no_);
     // Source_ is a transposed copy of input. It isn't needed if provided.
     external_source_ = input_transpose;
@@ -163,7 +174,7 @@ void FullyConnected::SetupForward(const NetworkIO& input,
 void FullyConnected::ForwardTimeStep(const double* d_input, const inT8* i_input,
                                      int t, double* output_line) {
   // input is copied to source_ line-by-line for cache coherency.
-  if (training() && external_source_ == NULL && d_input != NULL)
+  if (IsTraining() && external_source_ == NULL && d_input != NULL)
     source_t_.WriteStrided(t, d_input);
   if (d_input != NULL)
     weights_.MatrixDotVector(d_input, output_line);

lstm/fullyconnected.h

@@ -61,6 +61,10 @@ class FullyConnected : public Network {
     type_ = type;
   }
 
+  // Suspends/Enables training by setting the training_ flag. Serialize and
+  // DeSerialize only operate on the run-time data if state is false.
+  virtual void SetEnableTraining(TrainingState state);
+
   // Sets up the network for training. Initializes weights using weights of
   // scale `range` picked according to the random number generator `randomizer`.
   virtual int InitWeights(float range, TRand* randomizer);

lstm/lstm.cpp

@@ -102,6 +102,23 @@ StaticShape LSTM::OutputShape(const StaticShape& input_shape) const {
   return result;
 }
 
+// Suspends/Enables training by setting the training_ flag. Serialize and
+// DeSerialize only operate on the run-time data if state is false.
+void LSTM::SetEnableTraining(TrainingState state) {
+  if (state == TS_RE_ENABLE) {
+    if (training_ == TS_DISABLED) {
+      for (int w = 0; w < WT_COUNT; ++w) {
+        if (w == GFS && !Is2D()) continue;
+        gate_weights_[w].InitBackward(false);
+      }
+    }
+    training_ = TS_ENABLED;
+  } else {
+    training_ = state;
+  }
+  if (softmax_ != NULL) softmax_->SetEnableTraining(state);
+}
+
 // Sets up the network for training. Initializes weights using weights of
 // scale `range` picked according to the random number generator `randomizer`.
 int LSTM::InitWeights(float range, TRand* randomizer) {
@@ -148,7 +165,7 @@ bool LSTM::Serialize(TFile* fp) const {
   if (fp->FWrite(&na_, sizeof(na_), 1) != 1) return false;
   for (int w = 0; w < WT_COUNT; ++w) {
     if (w == GFS && !Is2D()) continue;
-    if (!gate_weights_[w].Serialize(training_, fp)) return false;
+    if (!gate_weights_[w].Serialize(IsTraining(), fp)) return false;
   }
   if (softmax_ != NULL && !softmax_->Serialize(fp)) return false;
   return true;
@@ -169,7 +186,7 @@ bool LSTM::DeSerialize(bool swap, TFile* fp) {
   is_2d_ = false;
   for (int w = 0; w < WT_COUNT; ++w) {
     if (w == GFS && !Is2D()) continue;
-    if (!gate_weights_[w].DeSerialize(training_, swap, fp)) return false;
+    if (!gate_weights_[w].DeSerialize(IsTraining(), swap, fp)) return false;
     if (w == CI) {
       ns_ = gate_weights_[CI].NumOutputs();
       is_2d_ = na_ - nf_ == ni_ + 2 * ns_;
@@ -322,7 +339,7 @@ void LSTM::Forward(bool debug, const NetworkIO& input,
     MultiplyAccumulate(ns_, temp_lines[CI], temp_lines[GI], curr_state);
     // Clip curr_state to a sane range.
     ClipVector<double>(ns_, -kStateClip, kStateClip, curr_state);
-    if (training_) {
+    if (IsTraining()) {
       // Save the gate node values.
       node_values_[CI].WriteTimeStep(t, temp_lines[CI]);
       node_values_[GI].WriteTimeStep(t, temp_lines[GI]);
@@ -331,7 +348,7 @@ void LSTM::Forward(bool debug, const NetworkIO& input,
       if (Is2D()) node_values_[GFS].WriteTimeStep(t, temp_lines[GFS]);
     }
     FuncMultiply<HFunc>(curr_state, temp_lines[GO], ns_, curr_output);
-    if (training_) state_.WriteTimeStep(t, curr_state);
+    if (IsTraining()) state_.WriteTimeStep(t, curr_state);
     if (softmax_ != NULL) {
       if (input.int_mode()) {
         int_output->WriteTimeStep(0, curr_output);
@@ -697,7 +714,7 @@ void LSTM::PrintDW() {
 void LSTM::ResizeForward(const NetworkIO& input) {
   source_.Resize(input, na_);
   which_fg_.ResizeNoInit(input.Width(), ns_);
-  if (training_) {
+  if (IsTraining()) {
     state_.ResizeFloat(input, ns_);
     for (int w = 0; w < WT_COUNT; ++w) {
       if (w == GFS && !Is2D()) continue;

lstm/lstm.h

@@ -69,6 +69,10 @@ class LSTM : public Network {
     return spec;
   }
 
+  // Suspends/Enables training by setting the training_ flag. Serialize and
+  // DeSerialize only operate on the run-time data if state is false.
+  virtual void SetEnableTraining(TrainingState state);
+
   // Sets up the network for training. Initializes weights using weights of
   // scale `range` picked according to the random number generator `randomizer`.
   virtual int InitWeights(float range, TRand* randomizer);

lstm/lstmrecognizer.cpp

@@ -253,7 +253,7 @@ bool LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
                                    float label_threshold, float* scale_factor,
                                    NetworkIO* inputs, NetworkIO* outputs) {
   // Maximum width of image to train on.
-  const int kMaxImageWidth = 2048;
+  const int kMaxImageWidth = 2560;
   // This ensures consistent recognition results.
   SetRandomSeed();
   int min_width = network_->XScaleFactor();
@@ -263,7 +263,7 @@ bool LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
     tprintf("Line cannot be recognized!!\n");
     return false;
   }
-  if (network_->training() && pixGetWidth(pix) > kMaxImageWidth) {
+  if (network_->IsTraining() && pixGetWidth(pix) > kMaxImageWidth) {
     tprintf("Image too large to learn!! Size = %dx%d\n", pixGetWidth(pix),
             pixGetHeight(pix));
     pixDestroy(&pix);