Publication Type:

Conference Paper

Source:

2013 12th International Conference on Document Analysis and Recognition (2013)

ISBN:

9780769549996

Accession Number:

13844764

Keywords:

camera-based character recognition, Character recognition, classifier, computational modeling, Data models, Devanagari character recognition, Devanagari characters, Devanagari consonants, Devanagari script, Devanagari vowels, DSIW2K, English, Feature extraction, handwritten dataset, image classification, India, NAtural language processing, New Delhi, Object recognition, part-based model, part-based model technique, printed dataset, street scene image, Support vector machines, Training, Vectors

Abstract:

Character recognition in scene images is an extremely challenging task. Although several techniques are reported performing well, they pertain to English only. This paper focuses on Devanagari character recognition from scene images. Devanagari script is very popular language and has very typical characteristics different from other scripts, particularly English. Combination of basic Devanagari consonants and vowels in multi-variegated ways can yield as many as 100s of characters. Building a classifier to recognize all these classes will be a difficult task. To alleviate this problem, a novel part-based model technique is proposed. 40 basic classes were identified from the Devanagari script for the same purpose. The technique was proposed so as to classify an instance of one these classes in any given test sample. Procuring a large dataset for training is not feasible in the case of scene images. To simultaneously solve this problem, we developed our technique that can use either the machine printed or the handwritten dataset for training. We present our results on the publicly available dataset (DSIW2K) containing images of street scenes taken in New Delhi, India.

Cite this Research Publication

V. Narang, Roy, S., Dr. Oruganti Venkata Ramana Murthy, and Hanmandlu, M., “Devanagari Character Recognition in Scene Images”, in 2013 12th International Conference on Document Analysis and Recognition, 2013.

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