Improved error-correcting from extracted handwritings in Chinese

Abstract

Errors exist in extracted Chinese handwritings even importing language models because of casualness and diversity of handwriting input, which would also affect the accuracy of recognition. Chinese handwritings cannot be converted into encoded texts until extracted and recognized correctly. Extracted handwritings may contain wrong language types, symbols, words, and word pairs. The conventional approach is based on context to adaptively correct theses errors. However, each writing character extraction candidates are fully visualized in bounding boxes, the overlaps of which bring more cognitive burden. Furthermore, the operation gesture needs to be accurate to stroke-level in convention that reduces the efficiency of correction. Therefore, an improved approach of error-correcting is proposed that an adaptive visualization as correcting reference and gesture analysis are taken into consideration. Experiments using real-life Chinese handwritings are conducted and compared the proposed approach with others. Experimental results demonstrate that the proposed approach is effective and robust.