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Scene Text Detection using Regression Tree Training


Bhumi Patel , parul institute of engineering,limda; Nneeta Chaudasama, parul institute of engineering


Conditional Random Field (CRF), Connected Component Analysis (CCA), Text Detection, Text Localization


Text detection and localization in natural scene images is important for content-based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, the variant of text font, size and line orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help segment candidate text components by local binarization. To efficiently filter out the non-text components, a conditional random field (CRF) model considering unary component properties and binary contextual component relationships with supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring manual tuning. Experimental results evaluated on the ICDAR2005 competition dataset show that our approach yields higher precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image dataset with promising results.

Other Details

Paper ID: IJSRDV3I40284
Published in: Volume : 3, Issue : 4
Publication Date: 01/07/2015
Page(s): 1028-1031

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