Reconhecimento de dígitos manuscritos usando os atributos de assinatura e classificador de Caminhos de Floresta Ótima
(Recognition of handwritten digits using the signature features and Optimum-Path Forest Classifier)
Gustavo Siebra Lopes (firstname.lastname@example.org)1, David Clifte da Silva (email@example.com)1, Antônio Wendell Oliveira Rodrigues (firstname.lastname@example.org)1, Pedro Pedrosa Rebouças Filho (email@example.com)1
1Instituto Federal de Educação, Ciência e Tecnologia do Ceará (IFCE), Fortaleza, Ceará, Brazil
This paper appears in: Revista IEEE América Latina
Publication Date: May 2016
Volume: 14, Issue: 5
There is a growing need for recognition of digits manuscripts for use in various situations, such as recognition of handwritten postal address digits for automated redirection of letters in the mail, acknowledgment of nominal values in bank checks. Recognition of handwritten digits faces great difficulty in dealing with intra-class variation due to different writing styles, different degrees of inclination of the characters. Optical character recognition systems, also known as OCR, identifying and recognizing printed characters through images, an already widespread functionality in scanners, mobile devices, among others. This paper presents the use of the classifier Optimum-Path Forest (OPF) applied in handwriting recognition digits. A new feature extraction method is proposed using signature of the characters, and the OPF algorithm is used in the classification. According to the results presented, it appears that the detection and recognition of characters are being carried out satisfactorily in the Manhattan distance stood out with an average accuracy of 99.53%, and get training times and test lower than the other methods such as It is the characteristic of OPF method.
Machine learning, Pattern Recognition, Digital Image Processing, Computer Vision, OCR Applications, Optimum-Path Forest, OPF
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