Using hidden Markov model for Chinese business card recognition
Yuan-Kai Wang, Kuo‐Chin Fan, Yau Tarng Juang, Tailin Chen · 2002
Business card recognition is a difficult problem. Characters in business card are small with diverse font types. An approach using the left-right hidden Markov model is proposed for business card recognition. The hidden Markov model will output a top-10 candidate list as its recognition result. A postprocessing stage is followed to improve the recognition result. The postprocessing stage uses a bigram table as linguistic information to search for the optimized recognition result from the top-10 candidate list. Our experiments are built on the recognition of company item and address item in Chinese business cards. Bigram table and hidden Markov models are trained with a telephony database. 100 address items and 30 company items are used for testing. Experimental results reveal the validity of our proposed method.