使用CNN作为Web服务提供的Tifinagh手写字符识别

Kadri Ouahab, A. Benyahia, Adel Abdelhadi
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引用次数: 4

摘要

许多云提供商提供非常高精度的服务来利用光学字符识别(OCR)。但是,没有任何提供者将Tifinagh光学字符识别(OCR)作为Web服务提供。已经提出了几个工作来构建强大的Tifinagh OCR。不幸的是,没有一个是作为Web服务开发的。在本文中,我们通过Google Colab提出了一种基于深度学习模型的Tifinagh手写识别web服务的新架构。为了实现我们的建议,我们使用了新版本的TensorFlow库和一个非常大的Tifinagh字符数据库,该数据库由来自拉巴特穆罕默德五世大学的60,000张图像组成。实验结果表明,基于张量处理单元的TensorFlow库为开发快速、高精度的Tifinagh OCR web服务提供了一个非常有前途的框架。结果表明,基于卷积神经网络的方法优于现有的基于支持向量机和极限学习机的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tifinagh Handwriting Character Recognition Using a CNN Provided as a Web Service
Many cloud providers offer very high precision services to exploit Optical Character Recognition (OCR). However, there is no provider offers Tifinagh Optical Character Recognition (OCR) as Web Services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a Web Service. In this paper, we present a new architecture of Tifinagh Handwriting Recognition as a web service based on a deep learning model via Google Colab. For the implementation of our proposal, we used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a Tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The results show that our method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machine.
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