A method for combining complementary techniques for document image segmentation

Image segmentation is a major task of handwritten document processing. Many of the proposed techniques for image segmentation are complementary, in the sense that each of them using a different approach, can solve different difficult problems such as overlapping, touching components, influence of author style etc. In this paper a combination method of different segmentation techniques is presented. Our goal is to exploit the segmentation results of complementary techniques and specific features of the initial image so as to generate improved segmentation results. Experimental results on handwriting line segmentation methods demonstrate the effectiveness of the proposed combination method

An Efficient Word Segmentation Technique for Historical and Degraded Machine-Printed Documents

Word segmentation is a crucial step for segmentation-free document analysis systems and is used for creating an index based on word matching. In this paper, we propose a novel methodology for word segmentation in historical and degraded machineprinted documents. The proposed technique faces problems such as having text of different size, having text and non-text areas lying very near and having non-straight and warped text lines. It is based on: (i) a dynamic run length smoothing algorithm that helps grouping together homogeneous text regions, (ii) noise and punctuation marks removal as well as on obstacle detection in order to facilitate the segmentation process and (iv) a draft text line estimation procedure that guides the final word segmentation result. After testing on numerous historical and degraded machineprinted documents, it has turned out that our methodology performs better compared to current state-of-the-art word segmentation techniques for historical and degraded machine-printed documents. 1