An adaptive approach to extract characters from digital ink text in Chinese based on extracted errors

Abstract

Extracting characters from digital ink text is an essential step which leads to more reliable recognition of text and also a prerequisite for structured editing. Casualness and diversity of handwriting input result in unsatisfied accuracy of extracted characters. Reprocessing the initial extracted characters based on context makes some considerable improvement. Therefore, this paper proposes an approach to adaptively extracting characters from digital ink text in Chinese based on extracted errors. The approach firstly classified the extracted errors in the primary extraction. According to different types of extracted errors, the approach gives different operations. Experimental data shows that the approach is effective.