Novel character segmentation method for overlapped Chinese handwriting recognition based on LSTM neural networks

Overlapped handwriting recognition is widely used to input text in smart devices since it allows to write continuous characters on an size-restricted screens. How to segment the stroke sequences into characters is a crucial step before recognition. It is currently formulated as a two-class classification problem merely evaluating on the relationships between a pair of adjacent strokes. To facilitate the long contextual dependency, the paper novelly presents the problem as a sequential classification problem. Firstly each adjacent stroke pair is expressed as a feature vector. Secondly a LSTM model is learned to encode the long contextual history information from massive data. Finally the model is propagated forward to predict the labels once new samples are fed. Experiments are conducted on a public online Chinese handwriting database. The results show that the proposed method outperforms the traditional ones with about 10 percent improvement in terms of both specificity and precision.