Evaluating the conventional and class-modular architectures feedforward neural network for handwritten word recognition

Evaluating the conventional and class-modular architectures feedforward neural network for handwritten word recognition

Kapp, M. N. and Freitas, C. O.D.A. and Nievola, J. C. and Sabourin, R.

Brazilian Symposium of Computer Graphic and Image Processing 2003

Abstract : We evaluate the use of the conventional architecture feedforward MLP (multiple layer perception) and class-modular for the handwriting recognition and it also compares the results obtained with previous works in terms of recognition rate. We present a feature set in full detail to work with handwriting recognition. The experiments showed that the class-modular architecture is better than conventional architecture. The obtained average recognition rates were 77.08% using the conventional architecture and 81.75% using the class-modular.