Research And Implementation Of Mongolian Online Handwriting Recognition Based On Words

Handwriting recognition is one of the significant topics of intelligent human-computer interaction,which can be divided into online handwriting recognition and offline handwriting recognition according to the method of recognition.Traditional Mongolian,as the language and script of Mongolian people in China,is a cultural treasure of minority people in China.The research of Mongolian handwriting recognition was conducted at the beginning of the 21 st century.Nowadays,the researches of offline Mongolian recognition are becoming mature.OCR software for traditional Mongolian recognition has appeared in the market for tasks such as recognition of Mongolian ancient books and printed documents.However,for Mongolian online handwriting recognition,there are still many shortcomings in the researches due to the arbitrary handwriting writing style,the difficulty of collecting effective data,and the difficulty of segmenting Mongolian handwritten characters.The study of Mongolian online handwriting recognition can strengthen the popularization and application of scientific and technological information technology in ethnic areas,help the development and application of Mongolian intelligent information technology,and is of great significance to the inheritance and protection of traditional Mongolian.This thesis presents an in-depth study of the task of online handwriting recognition in Mongolian,and the main work is as follows:1.A corpus of Mongolian handwriting data is constructed,and the resampling processing method in the preprocessing process is improved.A set of preprocessing processes applicable to Mongolian online handwriting coordinate sequences is proposed.The experimental results show that the method can optimize the data representation ability of handwritten samples and effectively improve the accuracy rate of Mongolian handwriting recognition.2.In this thesis,a sequence-to-sequence Mongolian online handwriting recognition model combining convolutional network,self-attentive model and attention mechanism is proposed to extract stroke segment and stroke features autonomously and perform segmentation-free character-level recognition.Meanwhile,a lexicon-guided beam search algorithm based on decoding is proposed to achieve Mongolian word recognition.The experimental comparison results show that the recognition model proposed in this thesis significantly improves the recognition rate of Mongolian online handwritten words,and the Top10 prediction results improve to89.77% on the test set.3.A Mongolian online handwriting recognition cloud service system was built.The system was designed with browser/server architecture,and a high concurrency web interface service was developed using Tornado,and embedded into Mongolian intelligent whole-word input method and other software to be widely used.