Document image segmentation using discriminative learning over connected components

Authors:
Syed Saqib Bukhari;Mayce Ibrahim Ali Al Azawi;Faisal Shafait;Thomas M. Breuel
Affiliations:
Technical University of Kaiserslautern, Germany;Technical University of Kaiserslautern, Germany;German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany;Technical University of Kaiserslautern, Kaiserslautern, Germany
Venue:
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Year:
2010

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Abstract

Segmentation of a document image into text and non-text regions is an important preprocessing step for a variety of document image analysis tasks, like improving OCR, document compression etc. Most of the state-of-the-art document image segmentation approaches perform segmentation using pixel-based or zone(block)-based classification. Pixel-based classification approaches are time consuming, whereas block-based methods heavily depend on the accuracy of block segmentation step. In contrast to the state-of-the-art document image segmentation approaches, our segmentation approach introduces connected component based classification, thereby not requiring a block segmentation beforehand. Here we train a self-tunable multi-layer perceptron (MLP) classifier for distinguishing between text and non-text connected components using shape and context information as a feature vector. Experimental results prove the effectiveness of our proposed algorithm. We have evaluated our method on subset of UW-III, ICDAR 2009 page segmentation competition test images and circuit diagrams datasets and compared its results with the state-of-the-art leptonica's page segmentation algorithm.