Recognizing Unconstrained Vietnamese Handwriting By Attention Based Encoder Decoder Model
Anh Duc Le, Hung Tuan Nguyen, Masaki Nakagawa · 2018
Inspired by recent successes in neural machine translation and image caption generation, we present an attention based encoder decoder model (AED) to recognize Vietnamese Handwritten Text. The model composes of three parts: a convolution neural network (CNN) for extracting invariant features, a Bidirectional Long Short-Term Memory network (BLSTM) for encoding extracted features (BLSTM encoder), and a Long Short-Term Memory network (LSTM) with an attention model incorporated for generating output text (LSTM decoder), which are connected from the CNN part to the BLSTM encoder and finally the LSTM decoder. The input of the CNN part is a handwritten text image and the target of the LSTM decoder is the corresponding text of the input image. Our model is trained end-to-end to predict the text from a given input image since all the parts are differential components. In the experiment section, we evaluate our proposed AED model on the VNOnDB-Word database to verify its efficiency. The experiential results show that our model achieves 12.30% of word error rate without using language model. This result is competitive with the handwriting recognition system provided by Google in the Vietnamese Online Handwritten Text Recognition competition.