手写体孤立孟加拉复合字识别

Asfi Fardous, Shyla Afroge
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引用次数: 16

摘要

孟加拉语复合字是孟加拉语字母表的一部分。没有开发更好的孟加拉语孤立复合字识别器,孟加拉语OCR是不完整的。目前对孟加拉语基本汉字和数字的研究较多,而对手写孟加拉语复合字的研究却很少。基于这一动机,我们开发了一种基于卷积神经网络(CNN)的模型来识别手写孤立的孟加拉语复合字。通过在CMATERdb 3.1.3.3上训练该模型,并将测试数据集上的结果与其他现有的手写体孟加拉复合字识别方法进行比较,分析了该模型的性能。该网络在测试数据集上的准确率为95.5%,优于目前的一些方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Handwritten Isolated Bangla Compound Character Recognition
Bangla compound characters are a part of Bangla alphabet. Without developing a better recognizer for BangIa isolated compound characters Bangla OCR is incomplete. Whereas most of the researches have been conducted for Bangla basic characters and numerals, little has been done for handwritten Bangla compound characters. From this motivation, a convolutional neural network (CNN) based model has been developed to recognize handwritten isolated Bangla compound characters. The performance of the proposed model has been analyzed by training it over CMATERdb 3.1.3.3 and comparing the result over test dataset with other existing methods for handwritten Bangla compound character recognition. The result of the network showed 95.5% accuracy on the test dataset which is better than some current approaches.
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