Online Japanese Handwriting Recognizers using Recurrent Neural Networks

This paper presents an attempt to recognize online isolated handwritten Japanese characters as well as text lines by Recurrent Neural Networks (RNNs). Although there are some successful studies on online Chinese handwriting recognition using RNNs, it is difficult to achieve high accuracy for Japanese due to many different types of characters such as kanji and kana. Moreover, training RNNs usually requires a large number of samples for each class of character. Hence, we apply five different transformation operations on the original samples to generate artificial samples with various deformations. For online handwritten Japanese text, we use the Kondate database, but it does not cover the whole Japanese character set. Thus, we generate random text lines using the sentences from corpora and isolated characters from the Nakayosi and Kuchibue databases. Besides, we employ a robust process with some preprocessing steps and the state-of-the-art online features to extract invariant features from handwritten patterns because the handwritten samples are merged from the different databases collected on different devices with various resolutions. For the recognition model, we have implemented the different Bidirectional Long Short-Term Memory Networks (BLSTM networks) for isolated character classification (sequence classification task) and handwritten text recognition (transcription task). The best model of the sequence classification task achieves an accuracy of 97.91% on Nakayosi and 97.74% on Kuchibue. The best model of transcription task performs a character recognition rate of 86.31% on Kondate and 83.15% on the generated text lines.