Document Image Segmentation using Discriminative Learning over Connected Components

Syed Saqib Bukhari; Mayce Al-Azawi; Faisal Shafait; Thomas Breuel
In: 9th IAPR Workshop on Document Analysis Systems. IAPR International Workshop on Document Analysis Systems (DAS-2010), June 9-11, Boston, MA, USA, ACM, 6/2010.


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 1 page segmentation algorithm.



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