crnn_ctc_ocr.Tensorflow

This software implements the Convolutional Recurrent Neural Network (CRNN), a combination of CNN, RNN and CTC loss for image-based sequence recognition tasks, such as scene text recognition and OCR.

"An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition" : https://arxiv.org/abs/1507.05717

More details for CRNN and CTC loss (in chinese): https://zhuanlan.zhihu.com/p/43534801

The crnn+seq2seq+attention ocr code can be found here bai-shang/crnn_seq2seq_ocr.PyTorch

Dependencies

All dependencies should be installed are as follow:

• Python2.7 or Python3
• tensorflow==1.8.0
• opencv-python
• numpy

Required packages can be installed with

pip install -r requirements.txt

Note: This software cannot run in the tensorflow lastest version r1.11.0 since it's modified the tf.contrib.rnn API.

Run demo

Asume your current work directory is "crnn_ctc_ocr.Tensorflow"：

cd path/to/your/crnn_ctc_ocr.Tensorflow/

Dowload pretrained model and extract it to your disc: GoogleDrive .

Export current work directory path into PYTHONPATH:

export PYTHONPATH=$PYTHONPATH:./

Run inference demo:

python tools/inference_crnn_ctc.py \
  --image_dir ./test_data/images/ --image_list ./test_data/image_list.txt \
  --model_dir /path/to/your/bs_synth90k_model/ 2>/dev/null

Result is:

Predict 1_AFTERSHAVE_1509.jpg image as: aftershave

Predict 2_LARIAT_43420.jpg image as: lariat

Train a new model

Data Preparation

• Firstly you need to download Synth90k dataset and extract it into a folder.

• Secondly supply a txt file to specify the relative path to the image data dir and it's corresponding text label.

For example: image_list.txt

90kDICT32px/1/2/373_coley_14845.jpg coley
90kDICT32px/17/5/176_Nevadans_51437.jpg nevadans

• Then you are suppose to convert your dataset into tensorflow records which can be done by

python tools/create_crnn_ctc_tfrecord.py \
  --image_dir path/to/90kDICT32px/ --anno_file path/to/image_list.txt --data_dir ./tfrecords/ \
  --validation_split_fraction 0.1

Note: make sure that images can be read from the path you specificed, such as:

path/to/90kDICT32px/1/2/373_coley_14845.jpg
path/to/90kDICT32px/17/5/176_Nevadans_51437.jpg
.......

All training image will be scaled into height 32 and write to tfrecord file.
The dataset will be divided into train and validation set and you can change the parameter to control the ratio of them.

Otherwise you can use the dowload_synth90k_and_create_tfrecord.sh script automatically create tfrecord:

cd ./data
sh dowload_synth90k_and_create_tfrecord.sh

Train model

python tools/train_crnn_ctc.py --data_dir ./tfrecords/ --model_dir ./model/ --batch_size 32

After several times of iteration you can check the output in terminal as follow:

During my experiment the loss drops as follow:

Evaluate model

python tools/eval_crnn_ctc.py --data_dir ./tfrecords/ --model_dir ./model/ 2>/dev/null