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首页> 外文期刊>Current Science: A Fortnightly Journal of Research >Repossession and recognition system: transliteration of antique Tamil Brahmi typescript
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Repossession and recognition system: transliteration of antique Tamil Brahmi typescript

机译：收回和识别系统：古董泰米尔·施泰格的音译

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摘要

Tamil is among the ancient languages in the world with a rich literature. Recognition of antique Tamil scripts is difficult and different from the present form of the language. The character recognition of Brahmi script poses a big challenge even today. In this paper, a new technique for extracting the features is proposed, and converting the ancient Tamil script into the present form. Initially, the system is implemented by performing the pre-processing steps. Then the characters are individually separated using the segmentation process. The processed image undergoes a new feature extraction technique, where the system applies a chi-square test to check whether all the zoning feature values of the image are independent or dependent. The characters are recognized from the extracted features using neural networks. NNTool is employed to train the featured image and the data are compared with the database to recognize the Brahmi characters. The feature extraction technique along with the neural network achieved recognition rate accuracy of 91.3% and error rate of 8.7% using the confusion matrix. Our experiment has been simulated using MATLAB.
机译：泰米尔语是世界上拥有丰富文献的古老语言之一。识别古董泰米尔文字很困难，而且与泰米尔语目前的形式不同。即使在今天，婆罗米文字的字符识别仍然是一个巨大的挑战。本文提出了一种新的特征提取技术，并将古代泰米尔文字转换成现在的形式。最初，系统是通过执行预处理步骤来实现的。然后使用分割过程将字符单独分离。处理后的图像将接受一种新的特征提取技术，系统将应用卡方检验来检查图像的所有分区特征值是独立的还是依赖的。使用神经网络从提取的特征中识别字符。使用NNTool对特征图像进行训练，并将数据与数据库进行比较，以识别婆罗米文字。特征提取技术与神经网络相结合，利用混淆矩阵实现了91.3%的识别准确率和8.7%的错误率。我们的实验已经用MATLAB进行了仿真。

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