Document Image Segmentation using Discriminative Learning over Connected Components

Syed Saqib Bukhari, Mayce Al-Azawi, Faisal Shafait, Thomas Breuel
9th IAPR Workshop on Document Analysis Systems, Boston, MA, USA, ACM, 6/2010

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 1 page segmentation algorithm.

Files:

  Bukhari-Text-Image-Segmentation-DAS10.pdf

BibTex:

@inproceedings{ BUKH2010,
	Title = {Document Image Segmentation using Discriminative Learning over Connected Components},
	Author = {Syed Saqib Bukhari and Mayce Al-Azawi and Faisal Shafait and Thomas Breuel},
	BookTitle = {9th IAPR Workshop on Document Analysis Systems},
	Month = {6},
	Year = {2010},
	Publisher = {ACM}
}

     
Last modified:: 30.08.2016