End-to-end system for printed Amazigh script recognition in document images

被引：0

作者：

Aharrane, Nabil ^{[1
]}

Dahmouni, Abdellatif ^{[1
]}

Ensah, Karim El Moutaouakil ^{[2
]}

Satori, Khalid ^{[1
]}

机构：

[1] USMBA Univ, LIIAN Lab, Fes, Morocco

[2] Univ Mohammed 1, ENSAH, Al Hoceima, Morocco

来源：

2017 3RD INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SIGNAL AND IMAGE PROCESSING (ATSIP) | 2017年

关键词：

Amazigh OCR; language identification; deep learning; convolutionnal neural network; machine learning;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

in this work, we present an end-to-end system devoted to automatic recognition of printed Amazigh script in complex document images containing different languages such as Web images and natural scene images. To this end, text extraction from images is performed; the extracted text serves as input for a trained convolutional neural network (CNN) to identify its language. Finally, we proceed to the recognition of the Amazigh text script using a developed optical character recognition (OCR) system. The CNN reaches 99,12% of accuracy while the OCR system gets 99,93% The obtained results seem to be very satisfactory.

引用

页码：313 / 318

页数：6

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