基于CNN的泰米尔棕榈叶手稿字符识别与分类

Pravin Savaridass M, Haritha J, B. T, Vairavel K S, Ikram N, Janani M, Indrajith K
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引用次数: 2

摘要

棕榈叶手稿是极其重要的,因为它们有丰富的信息来源。因此,必须提供对古代手稿的简单访问,以便与世界其他地方分享这些信息,并促进未来对古代文学的研究。本文研究了基于卷积神经网络(CNN)的光学字符识别(OCR)系统,用于泰米尔棕榈叶手稿字符的准确数字化和识别。本文采用了卷积神经网络的卷积层、池化层、激活层、全连接层和分类器。对棕榈叶手稿进行扫描,利用扫描图像生成字符集数据库。该数据库被分为67个独立的类,每个类包含大约100个单独的样本。说明了棕榈叶手稿的OCR识别及其相关问题。利用CNN模型实现了泰米尔棕榈叶手稿字符识别方法的一个实例。发现CNN模型具有更好的识别率。由于CNN的每一层都检索了大量的特征,所以预测率和准确率都很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNN Based Character Recognition and Classification in Tamil Palm Leaf Manuscripts
Palm leaf manuscripts are extremely important as they have a rich source of information. As a result, simple access to ancient manuscripts must be provided to share this information with the rest of the world and to promote future study into ancient literature. This study deals with Convolutional Neural Network (CNN)-based Optical Character Recognition (OCR) system for accurately digitizing and identifying the characters for Tamil palm leaf manuscripts. The convolution layer, pooling layer, activation layer, fully connected layer, and classifier of the convolutional neural network is employed in this article. Palm-leaf manuscripts were scanned and the scanned images are used to generate the character set database. The database is divided into 67 separate classes, each of which contains roughly 100 individual samples. OCR recognition of the palm leaf manuscripts and problems associated with this are illustrated. A working example of the character recognition method for Tamil palm-leaf manuscript was implemented using the CNN model. The CNN model was found to have a better recognition rate. The prediction rate and accuracy are great because of the large number of features retrieved for each layer of CNN.
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