Gabor-Based Recognizer for Chinese Handwriting from Segmentation-Free Strategy

摘要

Segmentation-free recognizer is presented to transcribe Chinese handwritten documents, incorporating Gabor features and Hidden Markov Models (HMMs). Textline is extracted and filtered as Gabor observations by sliding windows first. Then Baum-Welch algorithm is used to train character HMMs. Finally, best character string in maximizing a posteriori criterion is found out through Viterbi algorithm as output. Experiments are conducted on a collection of Chinese handwriting. The results not only show the evident feasibility of segmentation-free strategy, but also manifest the advantages of Gabor filters in the transcription of Chinese handwriting.