Title :
A knowledge-based approach for script recognition without training
Author_Institution :
Comput. Syst. & Commun. Group, Tata Inst. of Fundamental Res., Bombay, India
fDate :
12/1/1995 12:00:00 AM
Abstract :
The approach described is based on an empirical parametric model for the handwriting recognition system. The parameters are so chosen and quantized as to retain only broad shape information, ignoring writer-dependent and other variability. Concatenation of character prototypes generates archetypal reference words for recognition, and training is unnecessary. The recognition scores exceed 90%
Keywords :
character recognition; image coding; image segmentation; knowledge based systems; real-time systems; archetypal reference words; character recognition; cursive script recognition; decoding; empirical parametric model; encoding; handwriting recognition system; knowledge-based system; shape vectors; transition segments; Character generation; Character recognition; Decoding; Hidden Markov models; Parametric statistics; Pattern recognition; Prototypes; Shape; Transfer functions; Writing;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
