Amharic Character Image Recognition

B. Belay, T. Habtegebrial, D. Stricker
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引用次数: 17

Abstract

In this paper we introduce Convolutional Neural Network (CNN) based method for Amharic character image recognition. We also introduce a dataset for training purposes. The proposed method has less pre-processing steps and out per- forms the state-of-the-art by a large margin. Experiments were done on 80,000 Amharic character images which was generated with different degradation level. We systematically evaluated the performance of the recognition model and achieved the state-of- art performance with an average recognition accuracy of 92.71%.
阿姆哈拉语字符图像识别
本文介绍了一种基于卷积神经网络(CNN)的阿姆哈拉语字符图像识别方法。我们还引入了一个用于训练目的的数据集。该方法预处理步骤少,大大超出了目前的技术水平。对8万幅不同退化程度的阿姆哈拉语字符图像进行了实验。我们系统地评估了识别模型的性能,平均识别准确率为92.71%,达到了最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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