VELOSO, L. R.; http://lattes.cnpq.br/2498050002491677; VELOSO, Luciana Ribeiro.
Resumo:
This work presents a writer-dependent system for isolated handwritten cursive word
recognition. This system is characterized by the utilization of a pre-processing state,
which corrects imperfections and normalizes variations in the word image, an explicit
segmentation stage, which splits the word into characters or character segments, a feature
extraction stage, which represents the image by three feature vectors (perceptive,
global and directional features), and a vector quantization module, which performs the
mapping of a feature vector into an observation vector (or symbols vector). The symbols
correspond to indices (the code vectors) generated by the representation (vector
quantization) of the feature sequences with the use of dictionaries. Finally, there is the
classification stage, performed by Hidden Markov Models, where characters are individually recognized and combined to form a valid word. Experimental tests were conducted with a database specifically built for this problem, containing samples of manuscripts from 4 different writers. The writer-dependent system for isolated handwritten cursive word recognition was recognition rate between 83.31% and 92.96% depending writer analyzed. The results show that the system offers optimum performance when used
word recognize by the characters models.