| Summary: | The handwritten character recognition is still a major
challenge in the field of computer vision, primarily due to the diversity
of styles that people can write, which makes it difficult to generalize
the problem. In addition, there is also the difficulty in defining the
descriptors that best characterize the character and build high
performance OCR systems. This paper presents a system for
recognizing handwritten characters offline, using Artificial Neural
Networks Modular Class with classic backpropagation training
algorithm, besides the methods used for feature extraction. Although
training of neural classifiers require long processing and recognition of
62 classes of characters, few studies have considered the results
obtained from the experiments are shown very promising, achieving
hit rates above 90%.
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