光学字符识别分类器的性能分析

Vivank Sharma, Valaramathi B, K. Santhi, Shobhit Srivastava, Sumit Jahagirdar
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引用次数: 2

摘要

人脑可以很容易地识别手写的文字,而计算机仍在努力弄清楚它们是什么。大脑的这种惊人的力量需要被转移到计算机上,用于手写文本和手稿的自动数字化。本文提出的工作试图建立一个能够识别和预测不同印地语字符的模型,该模型可以进一步用于整个页面或文本的识别和数字化。我们的模型使用了不同的方法,如基线神经网络、卷积神经网络和简单的逻辑回归模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Analysis of the Classifiers for Optical Character Recognition
A brain can easily able to recognize handwritten words which computers are still struggling to figure out what they are. This amazing power of the brain requires to be transferred to a computer for automatic digitalization of handwritten texts and manuscript. The work proposed in this paper tried to make a model which is able to recognize and predict different Hindi character which can be further used to identify and digitalize the whole page or text. Our model uses different approaches like a base-line neural network, convolutional neural network, and a simple logistic regression model.
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