Recognition of Conjunctive Bangla Characters by Artificial Neural Network

A.R. Forkan, S. Saha, M. Rahman, A. Sattar
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引用次数: 11

Abstract

This paper is concerned with optical character recognition (OCR) system for Bangla conjunctive characters. A method is proposed giving emphasis on the identification of the characters using the proposed methodology. Here generalization is achieved by preprocessing the characters before presenting them to the system for classification. The pre-processing systematically functions isolating the characters from BMP images, as well as noise removal, scaling and binary image conversion. The method uses a flexible matching between sample data and training data components applying Multi-layered Feed-forward Artificial Neural Network. A number of parameters of Neural Network are estimated so that the system performance is improved in comparison with normal training. Classification of character is defined as error minimization among the possible training set. Also, a measure of the amount of distortion for this training is given. Application of Artificial Neural Network in character recognition has made the system faster with optimum performance.
基于人工神经网络的孟加拉连词识别
本文研究了孟加拉语连词的光学字符识别系统。提出了一种方法,重点是使用所提出的方法识别字符。在这里,泛化是通过在将字符呈现给系统进行分类之前对其进行预处理来实现的。预处理系统地实现了对BMP图像进行字符分离、去噪、缩放和二值图像转换等功能。该方法采用多层前馈人工神经网络对样本数据和训练数据进行灵活匹配。对神经网络的一些参数进行估计,使系统的性能比正常训练有所提高。字符的分类被定义为在可能的训练集中误差最小化。此外,还给出了该训练的失真量的度量。将人工神经网络应用于字符识别,使识别速度更快,性能更优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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