Arabic word recognizer for mobile applications

Nitin Khanna; Golnaz Abdollahian; Ben Brame; Mireille Boutin; Edward J. Delp

doi:10.1117/12.876810

7 February 2011 Arabic word recognizer for mobile applications

Nitin Khanna, Golnaz Abdollahian, Ben Brame, Mireille Boutin, Edward J. Delp

Proceedings Volume 7873, Computational Imaging IX; 78730J (2011) https://doi.org/10.1117/12.876810
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

When traveling in a region where the local language is not written using a "Roman alphabet," translating written text (e.g., documents, road signs, or placards) is a particularly difficult problem since the text cannot be easily entered into a translation device or searched using a dictionary. To address this problem, we are developing the "Rosetta Phone," a handheld device (e.g., PDA or mobile telephone) capable of acquiring an image of the text, locating the region (word) of interest within the image, and producing both an audio and a visual English interpretation of the text. This paper presents a system targeted for interpreting words written in Arabic script. The goal of this work is to develop an autonomous, segmentation-free Arabic phrase recognizer, with computational complexity low enough to deploy on a mobile device. A prototype of the proposed system has been deployed on an iPhone with a suitable user interface. The system was tested on a number of noisy images, in addition to the images acquired from the iPhone's camera. It identifies Arabic words or phrases by extracting appropriate features and assigning "codewords" to each word or phrase. On a dictionary of 5,000 words, the system uniquely mapped (word-image to codeword) 99.9% of the words. The system has a 82% recognition accuracy on images of words captured using the iPhone's built-in camera.

Citation Download Citation

Nitin Khanna, Golnaz Abdollahian, Ben Brame, Mireille Boutin, and Edward J. Delp "Arabic word recognizer for mobile applications", Proc. SPIE 7873, Computational Imaging IX, 78730J (7 February 2011); https://doi.org/10.1117/12.876810

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KEYWORDS

Associative arrays

Cameras

Image segmentation

Mobile devices

Optical character recognition

Binary data

Image quality

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