Coded a complete solution for OCR on images with the help of self attention. Attention-based OCR Visual attention-based OCR model for image recognition with additional tools for creating TFRecords datasets and exporting the trained model with weights as a SavedModel or a frozen graph.
The model first runs a sliding CNN on the image (images are resized to height 32 while preserving aspect ratio). Then an LSTM is stacked on top of the CNN. Finally, an attention model is used as a decoder for producing the final outputs.
OCR example
Usage Create a dataset To build a TFRecords dataset, you need a collection of images and an annotation file with their respective labels.
aocr dataset ./datasets/annotations-training.txt ./datasets/training.tfrecords aocr dataset ./datasets/annotations-testing.txt ./datasets/testing.tfrecords Annotations are simple text files containing the image paths (either absolute or relative to your working dir) and their corresponding labels:
datasets/images/hello.jpg hello datasets/images/world.jpg world Train aocr train ./datasets/training.tfrecords A new model will be created, and the training will start. Note that it takes quite a long time to reach convergence, since we are training the CNN and attention model simultaneously.
The --steps-per-checkpoint parameter determines how often the model checkpoints will be saved (the default output dir is checkpoints/).
Important: there is a lot of available training options. See the CLI help or the parameters section of this README.
Test and visualize aocr test ./datasets/testing.tfrecords Additionally, you can visualize the attention results during testing (saved to out/ by default):
aocr test --visualize ./datasets/testing.tfrecords Example output images in results/correct:
Image 0 (j/j):
example image 0
Image 1 (u/u):
example image 1
Image 2 (n/n):
example image 2
Image 3 (g/g):
example image 3
Image 4 (l/l):
example image 4
Image 5 (e/e):
example image 5
Export After the model is trained and a checkpoint is available, it can be exported as either a frozen graph or a SavedModel.
aocr export ./exported-model
aocr export --format=frozengraph ./exported-model Load weights from the latest checkpoints and export the model into the ./exported-model directory.
Note: During training, it is possible to pass parameters describing the dimensions of the input images (--max-width, --max-height, etc.). If you used them during training, make sure to also pass them to the export command. Otherwise the exported model will not work properly when serving (next section).
Serving Exported SavedModel can be served as a HTTP REST API using Tensorflow Serving. You can start the server by running following command:
tensorflow_model_server --port=9000 --rest_api_port=9001 --model_name=yourmodelname --model_base_path=./exported-model Note: tensorflow_model_server requires a sub-directory with the version number to be present and inside it the files exported in the previous step. So you need to manually move contents of exported-model into exported-model/1.
Now you can send a prediction request to the running server, for example:
curl -X POST
http://localhost:9001/v1/models/aocr:predict
-H 'cache-control: no-cache'
-H 'content-type: application/json'
-d '{
"signature_name": "serving_default",
"inputs": {
"input": { "b64": "" }
}
}'
REST API requires binary inputs to be encoded as Base64 and wrapped in an object containing b64 key. See 'Encoding binary values' in Tensorflow Serving documentation
Google Cloud ML Engine To train the model in the Google Cloud Machine Learning Engine, upload the training dataset into a Google Cloud Storage bucket and start a training job with the gcloud tool.
Set the environment variables:
export JOB_PREFIX="aocr"
export REGION="us-central1"
export GS_BUCKET="gs://aocr-bucket"
export DATASET_UPLOAD_PATH="training.tfrecords" Upload the training dataset: gsutil cp ./datasets/training.tfrecords $GS_BUCKET/$DATASET_UPLOAD_PATH Launch the ML Engine job: export NOW=$(date +"%Y%m%d_%H%M%S") export JOB_NAME="$JOB_PREFIX$NOW" export JOB_DIR="$GS_BUCKET/$JOB_NAME"
gcloud ml-engine jobs submit training $JOB_NAME
--job-dir=$JOB_DIR
--module-name=aocr
--package-path=aocr
--region=$REGION
--scale-tier=BASIC_GPU
--runtime-version=1.2
--
train $GS_BUCKET/$DATASET_UPLOAD_PATH
--steps-per-checkpoint=500
--batch-size=512
--num-epoch=20
Parameters
Global
log-path: Path for the log file.
Testing
visualize: Output the attention maps on the original image.
Exporting
format: Format for the export (either savedmodel or frozengraph).
Training
steps-per-checkpoint: Checkpointing (print perplexity, save model) per how many steps
num-epoch: The number of whole data passes.
batch-size: Batch size.
initial-learning-rate: Initial learning rate, note the we use AdaDelta, so the initial value does not matter much.
target-embedding-size: Embedding dimension for each target.
attn-num-hidden: Number of hidden units in attention decoder cell.
attn-num-layers: Number of layers in attention decoder cell. (Encoder number of hidden units will be attn-num-hidden*attn-num-layers).
no-resume: Create new weights even if there are checkpoints present.
max-gradient-norm: Clip gradients to this norm.
no-gradient-clipping: Do not perform gradient clipping.
gpu-id: GPU to use.
use-gru: Use GRU cells instead of LSTM.
max-width: Maximum width for the input images. WARNING: images with the width higher than maximum will be discarded.
max-height: Maximum height for the input images.
max-prediction: Maximum length of the predicted word/phrase.