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首页> 外文会议>Proceedings of international conference on VLSI, communication, advanced devices, signals amp; systems and networking >Handwritten Script Recognition Using DCT, Gabor Filter, and Wavelet Features at Word Level
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Handwritten Script Recognition Using DCT, Gabor Filter, and Wavelet Features at Word Level

机译：在单词级别使用DCT，Gabor滤波器和小波功能进行手写脚本识别

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摘要

In a country like India, many of the documents such as office letters, checks, envelopes, forms, and other types of manuscripts are multiscript in nature. A document consisting of English script and a regional script is quite common. Hence, automatic recognition of scripts present in a multiscript document has a variety of practical and commercial applications in banks, post offices, reservation counters, libraries, etc. In this paper, a multiple feature-based approach is presented to identify the script type from a multiscript document. Features are extracted using Gabor filters, discrete cosine Transform, and wavelets of Daube-chies family. Nine popular Indian scripts are considered for recognition in this paper. Experiments are performed to test the recognition accuracy of the proposed system at word level for bilingual scripts. Using neural network classifier, the average success rate is found to be 97 %.
机译：在印度这样的国家/地区，许多文件（例如，办公室信函，支票，信封，表格和其他类型的手稿）本质上都是多原稿的。由英文脚本和区域脚本组成的文档非常普遍。因此，多脚本文档中存在的脚本的自动识别在银行，邮局，订票柜台，图书馆等中具有多种实际和商业应用。在本文中，提出了一种基于多特征的方法来从中识别脚本类型。多脚本文档。使用Gabor滤波器，离散余弦变换和Daube-chies系列小波提取特征。本文考虑了九种流行的印度文字。进行实验以测试该系统在双语脚本的单词级别上的识别准确性。使用神经网络分类器，发现平均成功率为97％。

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