Handwritten Character Recognition from Images using CNN-ECOC

Abstract

Recently, deep learning and character recognition have drawn the attention of many researchers. The deep neural networks have state-of-the-art performance in solving many classification and recognition problems. The Optical Character Recognition (OCR) takes an optical image of character as input and produces the corresponding character as output. It has a wide range of applications including traffic surveillance, robotics, digitization of printed articles, etc. The OCR can be implemented by using Convolutional Neural Network (CNN), which is a popular deep neural network architecture. The traditional CNN classifiers are capable of learning the important 2D features present in the images and classify them, the classification is performed by using soft-max layer. In this article, we have presented OCR by combining CNN and Error Correcting Output Code (ECOC) classifier. The CNN is used for feature extraction and the ECOC is used for classification. In order to find suitable CNN for extracting features, which can be used in combination with ECOC classifier for recognition of handwritten characters accurately, several popular CNN classifiers have been explored. The CNN-ECOC are trained and validated by using NIST handwritten character image dataset. The simulation result shows that CNN-ECOC gives higher accuracy as compared to the traditional CNN classifier.