A Line-based representation for matching words

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 46-49.With the increase of the number of documents available in the digital environment, efficient access to the documents becomes crucial. Manual indexing of the documents is costly; however, and can be carried out only in limited amounts. Therefore, automatic analysis of documents is crucial. Although plenty of effort has been spent on optical character recognition (OCR), most of the existing OCR systems fail to address the challenge of recognizing characters in historical documents on account of the poor quality of old documents, the high level of noise factors, and the variety of scripts. More importantly, OCR systems are usually language dependent and not available for all languages. Word spotting techniques have been recently proposed to access the historical documents with the idea that humans read whole words at a time. In these studies the words rather than the characters are considered as the basic units. Due to the poor quality of historical documents, the representation and matching of words continue to be challenging problems for word spotting. In this study we address these challenges and propose a simple but effective method for the representation of word images by a set of line descriptors. Then, two different matching criteria making use of the line-based representation are proposed. We apply our methods on the word spotting and redif extraction tasks. The proposed line-based representation does not require any specific pre-processing steps, and is applicable to different languages and scripts. In word spotting task, our results provide higher scores than the existing word spotting studies in terms of retrieval and recognition performances. In the redif extraction task, we obtain promising results providing a motivation for further and advanced studies on Ottoman literary texts.Can, Ethem FatihM.S

Word matching using single closed contours for indexing handwritten historical documents

Effective indexing is crucial for providing convenient access to scanned versions of large collections of historically valuable handwritten manuscripts. Since traditional handwriting recognizers based on optical character recognition (OCR) do not perform well on historical documents, recently a holistic word recognition approach has gained in popularity as an attractive and more straightforward solution (Lavrenko et al. in proc. document Image Analysis for Libraries (DIAL’04), pp. 278–287, 2004). Such techniques attempt to recognize words based on scalar and profile-based features extracted from whole word images. In this paper, we propose a new approach to holistic word recognition for historical handwritten manuscripts based on matching word contours instead of whole images or word profiles. The new method consists of robust extraction of closed word contours and the application of an elastic contour matching technique proposed originally for general shapes (Adamek and O’Connor in IEEE Trans Circuits Syst Video Technol 5:2004). We demonstrate that multiscale contour-based descriptors can effectively capture intrinsic word features avoiding any segmentation of words into smaller subunits. Our experiments show a recognition accuracy of 83%, which considerably exceeds the performance of other systems reported in the literature

On Region Algebras, XML Databases, and Information Retrieval

This paper describes some new ideas on developing a logical algebra for databases that manage textual data and support information retrieval functionality. We describe a first prototype of such a system

Retrieving descriptive phrases from large amounts of free text

This paper presents a system that retrieves descriptive phrases of proper nouns from free text. Sentences holding the specified noun are ranked using a technique based on pattern matching, word counting, and sentence location. No domain specific knowledge is used. Experiments show the system able to rank highly those sentences that contain phrases describing or defining the query noun. In contrast to existing methods, this system does not use parsing techniques but still achieves high levels of accuracy. From the results of a large-scale experiment, it is speculated that the success of this simpler method is due to the high quantities of free text being searched. Parallels between this work and recent findings in the very large corpus track of TREC are drawn