A Novel Approach for Vietnamese Handwritten Text Recognition

Abstract

This paper presents a segment and recognize approach to recognize Vietnamese online handwritten text, which is inspired from divide and conquer algorithm. First, we propose two segmentation methods to divide a handwritten paragraph into multiple text lines (text line segmentation) and then multiple words (word segmentation). Secondly, an end to end deep neural network model is developed to recognize Vietnamese handwritten words. Our model is derived from the success of the recent deep neural network models for offline handwriting recognition on English, Chinese, and Japanese. Due to the fact that Vietnamese online handwritten patterns commonly consist of many delayed strokes which are caused by diacritic marks, our approach is to render the online patterns to offline images and recognize them by a deep neural network. Although the offline images rendered from the online patterns are not completely same as the real offline images, they are still good enough to recognize. Besides, the proposed line and word segmentation methods have achieved the segmentation accuracy of 96.67% for line segmentation and 89.47% for word segmentation. Using the segmented handwritten words, the connectionist temporal classification loss with combining of convolutional layers and long short term memory layer are employed. The best recognition accuracy is 95.31% for characters and 88.80% for words, which show the promising results and could be improved in future by further research on different neural network structures.