Recognizing online Arabic handwritten characters using a deep architecture

17 March 2017 Recognizing online Arabic handwritten characters using a deep architecture

Najiba Tagougui, Monji Kherallah

Author Affiliations +

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103410L (2017) https://doi.org/10.1117/12.2268419
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

Recognizing the online Arabic handwritten script has been gaining more interest because of the impressive advances in mobile device requiring more and more intelligent handwritten recognizers. Since it was demonstrated within many previous research that Deep Neural Networks (DNN) exhibit a great performance, we propose in this work a new system based on a DNN in which we try to optimize the training process by a smooth construct of the deep architecture. The Output’s error of each unit in the previous layer will be computed and only the smallest error will be maintained in the next iteration. This paper uses LMCA database for training and testing data. The experimental study reveals that our proposed DBNN using generated Bottleneck features can outperform state of the art online recognizers.

Citation Download Citation

Najiba Tagougui and Monji Kherallah "Recognizing online Arabic handwritten characters using a deep architecture", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410L (17 March 2017); https://doi.org/10.1117/12.2268419

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE