Evaluating the Conventional and Class-Modular Architectures Feedforward Neural Network for Handwritten Word Recognition
نویسندگان
چکیده
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.
منابع مشابه
A class-modular feedforward neural network for handwriting recognition
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