Local Features based Text Detection Techniques in Document Images
Sharmila M Kumari and Akshatha. Article: Local Features based Text Detection Techniques in Document Images. IJCA Proceedings on International Conference on Information and Communication Technologies ICICT(7):6-11, October 2014. Full text available. BibTeX
@article{key:article,
author = {M Sharmila Kumari and Akshatha},
title = {Article: Local Features based Text Detection Techniques in Document Images},
journal = {IJCA Proceedings on International Conference on Information and Communication Technologies},
year = {2014},
volume = {ICICT},
number = {7},
pages = {6-11},
month = {October},
note = {Full text available}
}
Abstract
Video text information plays an important role in semantic-based video analysis, indexing and retrieval. It is observed that the detection of texts in video remains as a challenging task due to its complex varying conditions. In this paper, we present a study on local features based text detection in document images and more focus is provided for text detection based on Laplacian method. The document image is convolved with Laplacian operator to filter the document image. Then the maximum gradient difference value is computed for each pixel to generate threshold. Based on the computed threshold, a binarized frame is obtained which highlights the text block. The candidate text block regions are further verified and refined that is, the corresponding region in the Sobel edge map of the input image undergoes projection profile analysis to determine the boundary of the text blocks. Finally, empirical rules are employed to eliminate false positives based on geometrical properties. In addition, a comparative study of the Laplacian method with a novel text detection and localization method based on Corner response and Multi scale edge based method for video text detection is made. The techniques are evaluated on documents taken from ICDAR 2003 robust reading and text locating database. Experimental results show that the Laplacian method is able to detect texts of different fonts, contrast and backgrounds. To give an objective comparison of the Laplacian approach, we have used detection rate and false positive rate as decision parameters and metrics.
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