Please use this identifier to cite or link to this item: https://doi.org/10.1587/transinf.E96.D.2235
Title: Scene character detection and recognition with cooperative multiple-hypothesis framework
Authors: Huang, R.
Shivakumara, P. 
Feng, Y.
Uchida, S.
Keywords: Cooperative multiple-hypothesis framework
Integration
OCR
Scene character
Voting
Issue Date: Oct-2013
Citation: Huang, R., Shivakumara, P., Feng, Y., Uchida, S. (2013-10). Scene character detection and recognition with cooperative multiple-hypothesis framework. IEICE Transactions on Information and Systems E96-D (10) : 2235-2244. ScholarBank@NUS Repository. https://doi.org/10.1587/transinf.E96.D.2235
Abstract: To handle the variety of scene characters, we propose a cooperative multiple-hypothesis framework which consists of an image operator set module, an Optical Character Recognition (OCR) module and an integration module. Multiple image operators activated by multiple parameters probe suspected character regions. The OCR module is then applied to each suspected region and returns multiple candidates with weight values for future integration. Without the aid of the heuristic rules which impose constraints on segmentation area, aspect ratio, color consistency, text line orientations, etc., the integration module automatically prunes the redundant detection/recognition and pads the missing detection/recognition. The proposed framework bridges the gap between scene character detection and recognition, in the sense that a practical OCR engine is effectively leveraged for result refinement. In addition, the proposed method achieves the detection and recognition at the character level, which enables dealing with special scenarios such as single character, text along arbitrary orientations or text along curves. We perform experiments on the benchmark ICDAR 2011 Robust Reading Competition dataset which includes a text localization task and a word recognition task. The quantitative results demonstrate that multiple hypotheses outperform a single hypothesis, and be comparable with state-of-the-art methods in terms of recall, precision, F-measure, character recognition rate, total edit distance and word recognition rate. Moreover, two additional experiments are conducted to confirm the simplicity of parameter setting in this proposal. Copyright © 2013 The Institute of Electronics, Information and Communication Engineers.
Source Title: IEICE Transactions on Information and Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/77916
ISSN: 09168532
DOI: 10.1587/transinf.E96.D.2235
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