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

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.