A Handwritten Character Extraction Algorithm for Multi-language Document Image

In this paper, we propose a novel method for extracting handwritten characters from multi-language document images, which may contain various types of characters, e.g. Chinese, English, Japanese or their mixture. Firstly, text patches in document image are segmented based on connected component analysis. Rules for merging connected components are chosen according to the results of language identification. Then features are extracted for each basic analysis unit-text patch. Genetic algorithm is applied for feature fusion and patch type classification. Finally, a Markov Random Field model is utilized as a post-processing step to further correct the misclassification of text patch type by considering the document context. Experimental results show that the proposed algorithm can apparently improve the performance of handwritten character extraction.