Paper: On-Line Cursive Handwriting Recognition Using Hidden Markov Models And Statistical Grammars

ACL ID H94-1086
Title On-Line Cursive Handwriting Recognition Using Hidden Markov Models And Statistical Grammars
Venue Human Language Technologies
Session Main Conference
Year 1994
Authors

The BYBLOS continuous speech recognition system is applied to on-line cursive handwriting recognition. By exploiting similarities between on-line cursive handwriting and continuous speech recogni- tion, we can use the same base system adapted to handwriting feature vectors instead of speech. The use of hidden Markov models obvi- ates the need for segmentation of the handwritten script sentences before recognition. To test our system, we collected handwritten sentences using text from the ARPA Airline Travel Information Ser- vice (ATIS) and the ARPA Wall Street Journal (WSJ) corpora. In an initial experiment on the ATIS data, a word error rate of 1.1% was achieved with a 3050-word lexicon, 52-character set, collected from one writer. In a subsequent writer-dependent test on the WSJ data, er...