Thu, 21 Aug 2008 14:17:15 BST CiteULike: tgyork's ocr http://www.citeulike.org/user/tgyork/tag/ocr CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/tgyork/article/2799352 <i>Pattern Recognition, Vol. 40, No. 2. (February 2007), pp. 389-397.</i><br /><br />We consider the problem of document binarization as a pre-processing step for optical character recognition (OCR) for the purpose of keyword search of historical printed documents. A number of promising techniques from the literature for binarization, pre-filtering, and post-binarization denoising were implemented along with newly developed methods for binarization: an error diffusion binarization, a multiresolutional version of Otsu's binarization, and denoising by despeckling. The OCR in the ABBYY FineReader 7.1 SDK is used as a black box metric to compare methods. Results for 12 pages from six newspapers of differing quality show that performance varies widely by image, but that the classic Otsu method and Otsu-based methods perform best on average. OCR binarization and image pre-processing for searching historical documents Maya Gupta Nathaniel Jacobson Eric Garcia doi:10.1016/j.patcog.2006.04.043 Pattern Recognition, Vol. 40, No. 2. (February 2007), pp. 389-397. 2008-05-14T15:59:17-00:00 2007 Pattern Recognition 40 2 389 397 background binarisation historical image-processing ocr preprocessing http://www.citeulike.org/user/tgyork/article/894586 <i>Pattern Recognition, Vol. 29, No. 7. (July 1996), pp. 1147-1160.</i><br /><br />We describe novel methods of feature extraction for recognition of single isolated character images. Our approach is flexible in that the same algorithms can be used, without modification, for feature extraction in a variety of OCR problems. These include handwritten, machine-print, grayscale, binary and low-resolution character recognition. We use the gradient representation as the basis for extraction of low-level, structural and stroke-type features. These algorithms require a few simple arithmetic operations per image pixel which makes them suitable for real-time applications. A description of the algorithms and experiments with several data sets are presented in this paper. Experimental results using artificial neural networks are presented. Our results demonstrate high performance of these features when tested on data sets distinct from the training data. Gradient-based contour encoding for character recognition Geetha Srikantan Stephen Lam Sargur Srihari doi:10.1016/0031-3203(95)00146-8 Pattern Recognition, Vol. 29, No. 7. (July 1996), pp. 1147-1160. 2006-10-12T20:22:21-00:00 1996 Pattern Recognition 29 7 1147 1160 background character-based features ocr http://www.citeulike.org/user/tgyork/article/2098806 <i>Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on (1999), pp. 765-768.</i><br /><br />Investigates the automatic reading of unconstrained omni-writer handwritten texts. This paper shows how to endow the reading system with adaptation faculties for each writer's handwriting. The adaptation principles are of major importance for making robust decisions when neither simple lexical nor syntactic rules can be used, e.g. for a free lexicon or for full text recognition. The first part of this paper defines the concept of writer's invariants. In the second part, we explain how the recognition system can be adapted to a particular handwriting by exploiting the graphical context defined by the writer's invariants. This adaptation is guaranteed, thanks to the writer's invariants, by activating interaction links over the whole text between the recognition procedures for word entities and those for letter entities Defining writer's invariants to adapt the recognition task A Nosary L Heutte T Paquet Y Lecourtier Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on (1999), pp. 765-768. 2007-12-12T13:30:46-00:00 1999 Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on 765 768 background codebook interlending invariants ocr http://www.citeulike.org/user/tgyork/article/1952949 <i>(2004), pp. 369-376.</i> A search engine for historical manuscript images Toni Rath R Manmatha Victor Lavrenko doi:10.1145/1008992.1009056 (2004), pp. 369-376. 2007-11-21T16:46:20-00:00 2004 369 376 ACM features historical ocr search-engine http://www.citeulike.org/user/tgyork/article/816388 <i>Pattern Recognition, Vol. 29, No. 4. (April 1996), pp. 641-662.</i><br /><br />This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application. Feature extraction methods for character recognition-A survey Due Anil Jain Torfinn Taxt doi:10.1016/0031-3203(95)00118-2 Pattern Recognition, Vol. 29, No. 4. (April 1996), pp. 641-662. 2006-08-25T07:00:22-00:00 1996 Pattern Recognition 29 4 641 662 character-based ocr preprocessing recognition http://www.citeulike.org/user/tgyork/article/1724221 <i></i> Using Dynamic Time Warping for intuitive handwriting recognition R Niels L Vuurpijl 2007-10-03T14:05:45-00:00 background ocr online project