This is the implementation of the paper "SCAN: Sequence-Character Aware Network for Text Recogntion ". SCAN starts by locating and recognizing the characters, and then generates the word using a sequence-based approach. It comprises two modules: a semantic-segmentation-based character prediction, and an encoder-decoder network for word generation. The training is done over two stages. In the first stage, we adopt a multi-task training technique with both character-level and word-level losses and trainable loss weighting. In the second stage, the characterlevel loss is removed, enabling the use of data with only word-level annotations.
- Python 3.6
- numpy
- opencv-python
- keras 2.2.4
- tensorflow 1.14
- keras-self-attention