Printed Arabic Text Recognition using Linear and Nonlinear Regression
This addresses the need for improved optical character recognition for Arabic, a widely spoken language, but the approach appears incremental as it adapts regression methods to a specific domain.
The paper tackles the problem of recognizing printed Arabic text, which is challenging due to the cursive nature of the script, by proposing a technique based on linear and ellipse regression, achieving an average recognition rate of 86% on over 14,000 words.
Arabic language is one of the most popular languages in the world. Hundreds of millions of people in many countries around the world speak Arabic as their native speaking. However, due to complexity of Arabic language, recognition of printed and handwritten Arabic text remained untouched for a very long time compared with English and Chinese. Although, in the last few years, significant number of researches has been done in recognizing printed and handwritten Arabic text, it stills an open research field due to cursive nature of Arabic script. This paper proposes automatic printed Arabic text recognition technique based on linear and ellipse regression techniques. After collecting all possible forms of each character, unique code is generated to represent each character form. Each code contains a sequence of lines and ellipses. To recognize fonts, a unique list of codes is identified to be used as a fingerprint of font. The proposed technique has been evaluated using over 14000 different Arabic words with different fonts and experimental results show that average recognition rate of the proposed technique is 86%.