Reconhecimento de caracteres numéricos manuscritos usando a transformada Wavelet.

Abstract

The automatic recognition of handwritten numerical characters has been a research topic extensively studied in the last two decades because both of its theoretical value in pattern recognition and its potential for applications, such as in automatically processing postal ZIP codes from mail pieces and money amount from bankchecks. However, it still remains a difficult problem due to the large degree of variability the handwritten data may exihibit. The wavelet transform is a new tool developed in recent years, with the localization properties in both time and frequency domains, which simultaneously provides global and directional features about the numerals images. In this work, a novel approach for recognition of handwritten numerals is proposed, consisting of three stages: pre-processing, feature extraction and classification. Preprocessing deals with image normalization. Feature extraction aims to represent the normalized images by wavelets coefficients. Classification performs the final decision using a multilayer cluster neural network trained with the backpropagation algorithm. Experiments were realized with the wavelet families Haar, Daubechies, Coifiets and Cohen-Daubechies-Feauveau using the characters of the numerical database of CENPARMI. Results obtained show that the proposed method yields good performance.