Abstract
This paper presents an extensive investigation of the use of trigraphs for on-line cursive handwriting recognition based on Hidden Markov Models (HMMs). Trigraphs are context dependent HMMs representing a single written character in its left and right context, similar to triphones in speech recognition. Looking at the great success of triphones in continuous speech recognition, it was always a challenging and open question, if the introduction of trigraphs could lead to substantially improved handwriting recognition systems. The results of this investigation are indeed extremely encouraging: The introduction of suitable trigraphs led to a 50% relative error reduction for a writer dependent 1000 word handwriting recognition system, and to a 35% relative error reduction for the same system with an extended 30000 word vocabulary for cursive handwriting recognition.
Original language | English |
---|---|
Pages (from-to) | 3373-3376 |
Number of pages | 4 |
Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
Volume | 4 |
State | Published - 1997 |
Externally published | Yes |
Event | Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger Duration: 21 Apr 1997 → 24 Apr 1997 |