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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Multi-script and Multi-oriented Text Localization from Scene Images
verfasst von : Thotreingam Kasar, Angarai G. Ramakrishnan
Erschienen in: Camera-Based Document Analysis and Recognition
Verlag: Springer Berlin Heidelberg
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This paper describes a new method of color text localization from generic scene images containing text of different scripts and with arbitrary orientations. A representative set of colors is first identified using the edge information to initiate an unsupervised clustering algorithm. Text components are identified from each color layer using a combination of a support vector machine and a neural network classifier trained on a set of low-level features derived from the geometric, boundary, stroke and gradient information. Experiments on camera-captured images that contain variable fonts, size, color, irregular layout, non-uniform illumination and multiple scripts illustrate the robustness of the method. The proposed method yields precision and recall of 0.8 and 0.86 respectively on a database of 100 images. The method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset.