Optical character recognition using template matching and back propagation algorithm

Ashima Singh, Swapnil R. Desai
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引用次数: 16

Abstract

Building an effective methodology to detect characters from images with less error rate is the great task. Our aim is to furnish such an algorithm that will be able to generate error free recognition of text from the given input image which will help in document digitizing and prevention to the hand written text recognition. OCR has been in the intensive research topic for more than 4 decades, it is probably the most time consuming and labor intensive work of inputting the data through keyboard. This paper discuss about mechanical or electronic conversion of scanned images, text which contain graphics, image captured by camera, scanned images and the recognition of images where characters may be broken or smeared. The optical character recognition is the desktop based application developed using Java IDE and mysql as a database. We have gain 91.82% accuracy when applied on different data sets, in pre-processing we used different techniques to remove noise from the image in post processing we used dictionary for the characters which are not recognized during classification, in classification we have used the back propagation algorithm for the training of neural network, feature extraction has been performed by template matching and hamming distance. All the algorithms have been developed in java technology.
基于模板匹配和反向传播算法的光学字符识别
建立一种有效的方法,以较低的错误率从图像中检测字符是一项重要的任务。我们的目标是提供这样一种算法,能够从给定的输入图像中生成无错误的文本识别,这将有助于文档数字化和防止手写文本识别。OCR已经被研究了40多年,通过键盘输入数据可能是最耗时和劳动强度最大的工作。本文讨论了扫描图像、包含图形的文本、相机捕获的图像、扫描图像的机械或电子转换以及字符可能被破坏或污损的图像的识别。光学字符识别是使用Java IDE和mysql作为数据库开发的桌面应用程序。应用于不同的数据集,准确率达到了91.82%,预处理中采用了不同的技术去除图像中的噪声,后处理中对分类中未识别的字符使用字典,分类中使用反向传播算法训练神经网络,通过模板匹配和汉明距离进行特征提取。所有的算法都是用java技术开发的。
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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