Handwritten address recognition with open vocabulary using character n-grams
Tóm tắt
In this paper a recognition system, based on tied-mixture hidden Markov models, for handwritten address words is described, which makes use of a language model that consists of backoff character n-grams. For a dictionary-based recognition system it is essential that the structure of the address (name, street, city) is known. If the single parts of the address cannot be categorized, the used vocabulary is unknown and thus unlimited. The performance of this open vocabulary recognition using n-grams is compared to the use of dictionaries of different sizes. Especially, the confidence of recognition results and the possibility of a useful post-processing are significant advantages of language models.
Từ khóa
#Character recognition #Handwriting recognition #Vocabulary #Dictionaries #Hidden Markov models #Cities and towns #Streaming media #Automation #Writing #Postal servicesTài liệu tham khảo
willett, 2000, Ducoderthe duisburg university lvscr stackdecoder, Proc IEEE Int Conf on Acoustics Speech and Signal Processing (ICASSP), 1555
10.1007/s100320050040
10.1109/ICDAR.2001.953913
10.1109/ICDAR.1993.395706
10.1109/ICDAR.2001.953911
10.1109/34.771314
sch?ubler, 1998, A hmm-based system for recognition of handwritten adresswords, 6th Int Workshop on Frontiers in Handwriting Recognition (IWFHR), 505
10.1109/ICASSP.1992.225981
10.1109/ICDAR.1999.791791
10.1109/ICDAR.1997.620562
clarkson, 1997, Statistical language modeling using the cmu-cambridge toolkit, Proc EUROSPEECH, 2707
10.1109/5.880083
10.1109/MASSP.1986.1165342