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Evaluating the Conventional and Class-Modular Architectures Feedforward Neural Network for Handwritten Word Recogntion

Marcelo N. KappCinthia FreitasJúlio NievolaRobert Sabourin

This paper evaluates the use of the conventional architecture feedforward MLP (multiple layer perceptron) and class-modular for the handwriting recognition and it also compares the results obtained with previous works in terms of recognition rate. This work presents 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.

http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedin

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