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.
Hierarchical models with HMM has the advantage of recognizing Chinese characters in digital ink from non-native language writers. However, the recognition performance has been limited by the attribute of generative model of HMM. In this paper, we apply Hidden Conditional Random Field to improve the performance of hierarchical models. First, strokes in one Chinese character are classified with HCRF and then concatenated to the stroke symbol sequence. In the meantime, the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The approach proposed is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.
The extraction of errors is an important aspect of Chinese character writing research. Stroke errors are the origins of most handwriting mistakes. Previous works have made some efforts on the types of errors extracted, while most of them are either preset by rules or deficient to include all types of stroke errors. For foreign students learning Chinese as a foreign language, especially beginners whose writing habits and characteristics are affected by ones of their native languages, methods by presetting are difficult to adopt. Therefore, this paper initiated from the data itself, proposes an adaptive approach to extract handwriting errors based on the result of the stroke matching which is accurate to sampling points in strokes. After the tagging list given as a matching index, the writing errors are adaptively extracted in different stroke errors of Chinese characters, including missing strokes, extra strokes, concatenated strokes, broken strokes, redundant strokes, incomplete strokes, the error of orientation and order. After serial experiments, the result indicates that the proposed approach is effective in extracting handwriting stroke errors.
While Chinese is learned as a second language, its characters are taught step by step from their strokes to components, radicals to components, and their complex relations. Chinese Characters in digital ink from non-native language writers are deformed seriously, thus the global recognition approaches are poorer. So a progressive approach from bottom to top is presented based on hierarchical models. Hierarchical information includes strokes and hierarchical components. Each Chinese character is modeled as a hierarchical tree. Strokes in one Chinese characters in digital ink are classified with Hidden Markov Models and concatenated to the stroke symbol sequence. And then the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The method of this paper is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.
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