Added ability to randomly rotate images upside-down during training f…

…or training OSD

lstm/lstmrecognizer.cpp

@@ -187,8 +187,8 @@ void LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
   NetworkIO outputs;
   float scale_factor;
   NetworkIO inputs;
-  if (!RecognizeLine(image_data, invert, debug, false, &scale_factor, &inputs,
-                     &outputs))
+  if (!RecognizeLine(image_data, invert, debug, false, false, &scale_factor,
+                     &inputs, &outputs))
     return;
   if (search_ == NULL) {
     search_ =
@@ -227,7 +227,7 @@ void LSTMRecognizer::OutputStats(const NetworkIO& outputs, float* min_output,
 // Recognizes the image_data, returning the labels,
 // scores, and corresponding pairs of start, end x-coords in coords.
 bool LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
-                                   bool debug, bool re_invert,
+                                   bool debug, bool re_invert, bool upside_down,
                                    float* scale_factor, NetworkIO* inputs,
                                    NetworkIO* outputs) {
   // Maximum width of image to train on.
@@ -247,6 +247,7 @@ bool LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
     pixDestroy(&pix);
     return false;
   }
+  if (upside_down) pixRotate180(pix, pix);
   // Reduction factor from image to coords.
   *scale_factor = min_width / *scale_factor;
   inputs->set_int_mode(IsIntMode());

lstm/lstmrecognizer.h

@@ -199,8 +199,8 @@ class LSTMRecognizer {
   // forward outputs for the best photometric interpretation.
   // inputs is filled with the used inputs to the network.
   bool RecognizeLine(const ImageData& image_data, bool invert, bool debug,
-                     bool re_invert, float* scale_factor, NetworkIO* inputs,
-                     NetworkIO* outputs);
+                     bool re_invert, bool upside_down, float* scale_factor,
+                     NetworkIO* inputs, NetworkIO* outputs);
 
   // Converts an array of labels to utf-8, whether or not the labels are
   // augmented with character boundaries.

lstm/lstmtrainer.cpp

@@ -71,7 +71,8 @@ const int kTargetXScale = 5;
 const int kTargetYScale = 100;
 
 LSTMTrainer::LSTMTrainer()
-    : training_data_(0),
+    : randomly_rotate_(false),
+      training_data_(0),
       file_reader_(LoadDataFromFile),
       file_writer_(SaveDataToFile),
       checkpoint_reader_(
@@ -88,7 +89,8 @@ LSTMTrainer::LSTMTrainer(FileReader file_reader, FileWriter file_writer,
                          CheckPointWriter checkpoint_writer,
                          const char* model_base, const char* checkpoint_name,
                          int debug_interval, inT64 max_memory)
-    : training_data_(max_memory),
+    : randomly_rotate_(false),
+      training_data_(max_memory),
       file_reader_(file_reader),
       file_writer_(file_writer),
       checkpoint_reader_(checkpoint_reader),
@@ -296,7 +298,9 @@ void LSTMTrainer::DebugNetwork() {
 // tesseract into memory ready for training. Returns false if nothing was
 // loaded.
 bool LSTMTrainer::LoadAllTrainingData(const GenericVector<STRING>& filenames,
-                                      CachingStrategy cache_strategy) {
+                                      CachingStrategy cache_strategy,
+                                      bool randomly_rotate) {
+  randomly_rotate_ = randomly_rotate;
   training_data_.Clear();
   return training_data_.LoadDocuments(filenames, cache_strategy, file_reader_);
 }
@@ -838,6 +842,23 @@ Trainability LSTMTrainer::PrepareForBackward(const ImageData* trainingdata,
             trainingdata->language().string());
     return UNENCODABLE;
   }
+  bool upside_down = false;
+  if (randomly_rotate_) {
+    // This ensures consistent training results.
+    SetRandomSeed();
+    upside_down = randomizer_.SignedRand(1.0) > 0.0;
+    if (upside_down) {
+      // Modify the truth labels to match the rotation:
+      // Apart from space and null, increment the label. This is changes the
+      // script-id to the same script-id but upside-down.
+      // The labels need to be reversed in order, as the first is now the last.
+      for (int c = 0; c < truth_labels.size(); ++c) {
+        if (truth_labels[c] != UNICHAR_SPACE && truth_labels[c] != null_char_)
+          ++truth_labels[c];
+      }
+      truth_labels.reverse();
+    }
+  }
   int w = 0;
   while (w < truth_labels.size() &&
          (truth_labels[w] == UNICHAR_SPACE || truth_labels[w] == null_char_))
@@ -850,8 +871,8 @@ Trainability LSTMTrainer::PrepareForBackward(const ImageData* trainingdata,
   float image_scale;
   NetworkIO inputs;
   bool invert = trainingdata->boxes().empty();
-  if (!RecognizeLine(*trainingdata, invert, debug, invert, &image_scale,
-                     &inputs, fwd_outputs)) {
+  if (!RecognizeLine(*trainingdata, invert, debug, invert, upside_down,
+                     &image_scale, &inputs, fwd_outputs)) {
     tprintf("Image not trainable\n");
     return UNENCODABLE;
   }

lstm/lstmtrainer.h

@@ -182,7 +182,8 @@ class LSTMTrainer : public LSTMRecognizer {
   // tesseract into memory ready for training. Returns false if nothing was
   // loaded.
   bool LoadAllTrainingData(const GenericVector<STRING>& filenames,
-                           CachingStrategy cache_strategy);
+                           CachingStrategy cache_strategy,
+                           bool randomly_rotate);
 
   // Keeps track of best and locally worst error rate, using internally computed
   // values. See MaintainCheckpointsSpecific for more detail.
@@ -409,6 +410,7 @@ class LSTMTrainer : public LSTMRecognizer {
   // Checkpoint filename.
   STRING checkpoint_name_;
   // Training data.
+  bool randomly_rotate_;
   DocumentCache training_data_;
   // Name to use when saving best_trainer_.
   STRING best_model_name_;

training/lstmtraining.cpp

@@ -60,6 +60,8 @@ STRING_PARAM_FLAG(traineddata, "",
 STRING_PARAM_FLAG(old_traineddata, "",
                   "When changing the character set, this specifies the old"
                   " character set that is to be replaced");
+BOOL_PARAM_FLAG(randomly_rotate, false,
+                "Train OSD and randomly turn training samples upside-down");
 
 // Number of training images to train between calls to MaintainCheckpoints.
 const int kNumPagesPerBatch = 100;
@@ -167,9 +169,11 @@ int main(int argc, char **argv) {
       trainer.set_perfect_delay(FLAGS_perfect_sample_delay);
     }
   }
-  if (!trainer.LoadAllTrainingData(
-          filenames, FLAGS_sequential_training ? tesseract::CS_SEQUENTIAL
-                                               : tesseract::CS_ROUND_ROBIN)) {
+  if (!trainer.LoadAllTrainingData(filenames,
+                                   FLAGS_sequential_training
+                                       ? tesseract::CS_SEQUENTIAL
+                                       : tesseract::CS_ROUND_ROBIN,
+                                   FLAGS_randomly_rotate)) {
     tprintf("Load of images failed!!\n");
     return 1;
   }