@conference {13556,
	title = {Flattening curved documents in images},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {2},
	year = {2005},
	month = {2005/06//},
	pages = {338 - 345 vol. 2 - 338 - 345 vol. 2},
	abstract = {Compared to scanned images, document pictures captured by camera can suffer from distortions due to perspective and page warping. It is necessary to restore a frontal planar view of the page before other OCR techniques can be applied. In this paper we describe a novel approach for flattening a curved document in a single picture captured by an uncalibrated camera. To our knowledge this is the first reported method able to process general curved documents in images without camera calibration. We propose to model the page surface by a developable surface, and exploit the properties (parallelism and equal line spacing) of the printed textual content on the page to recover the surface shape. Experiments show that the output images are much more OCR friendly than the original ones. While our method is designed to work with any general developable surfaces, it can be adapted for typical special cases including planar pages, scans of thick books, and opened books.},
	keywords = {calibration;, camera, character, content;, curved, distortion;, document, document;, image, images;, OCR, optical, page, pictures;, printed, processing;, recognition;, restoration;, scanned, techniques;, textual, warping;},
	doi = {10.1109/CVPR.2005.163},
	author = {Liang,Jian and DeMenthon,D. and David Doermann}
}
@conference {13625,
	title = {Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {252 - 255 Vol.1 - 252 - 255 Vol.1},
	abstract = {In combining multiple classifiers using a Bayesian formalism, a high dimensional probability distribution is composed of a class and decisions of classifiers. In order to do product approximation of the probability distribution, the upper bound of Bayes error rate, bounded by the conditional entropy of a class and decisions, should be minimized. A second-order dependency-based product approximation is proposed in this paper by considering the second-order dependency between the class and decisions. The proposed method is evaluated by combining the classifiers recognizing unconstrained handwritten numerals.},
	keywords = {approximation;, Bayes, Bayesian, bound, character, classification;, classifiers;, conditional, distribution;, entropy;, error, formalism;, handwritten, methods;, minimisation;, multiple, numerals;, pattern, probability, probability;, Product, rate;, recognition;, statistics;, unconstrained, upper},
	doi = {10.1109/ICPR.2004.1334071},
	author = {Kang,Hee-Joong and David Doermann}
}
@conference {13566,
	title = {Hidden loop recovery for handwriting recognition},
	booktitle = {Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop on},
	year = {2002},
	month = {2002///},
	pages = {375 - 380},
	abstract = {One significant challenge in the recognition of off-line handwriting is in the interpretation of loop structures. Although this information is readily available in online representation, close proximity of strokes often merges their centers making them difficult to identify. In this paper a novel approach to the recovery of hidden loops in off-line scanned document images is presented. The proposed algorithm seeks blobs that resemble truncated ellipses. We use a sophisticated form analysis method based on mutual distance measurements between the two sides of a symmetric shape. The experimental results are compared with the ground truth of the online representations of each off-line word image. More than 86\% percent of the meaningful loops are handled correctly.},
	keywords = {analysis;, character, contour, cursive, detection;, distance, edge, ellipses;, form, handwritten, hidden, loop, measurements;, mutual, partitioning;, recognition;, recovery;, SHAPE, shape;, sophisticated, strokes;, symmetric, truncated, word},
	doi = {10.1109/IWFHR.2002.1030939},
	author = {David Doermann and Intrator,N. and Rivin,E. and Steinherz,T.}
}
@conference {13618,
	title = {Page classification through logical labelling},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {477 - 480 vol.3 - 477 - 480 vol.3},
	abstract = {We propose an integrated approach to page classification and logical labelling. Layout is represented by a fully connected attributed relational graph that is matched to the graph of an unknown document, achieving classification and labelling simultaneously. By incorporating global constraints in an integrated fashion, ambiguity at the zone level can be reduced, providing robustness to noise and variation. Models are automatically trained from sample documents. Experimental results show promise for the classification and labelling of technical article title pages, and supports the idea of a hierarchical model base.},
	keywords = {article, attributed, base;, character, classification;, constraints;, document, document;, experimental, global, graph, graph;, hierarchical, image, images;, labelling;, logical, model, noise;, OCR;, optical, page, pages;, processing;, recognition;, relational, results;, technical, theory;, title, unknown},
	doi = {10.1109/ICPR.2002.1047980},
	author = {Liang,Jian and David Doermann and Ma,M. and Guo,J. K}
}
@conference {18156,
	title = {A recognition algorithm for Chinese characters in diverse fonts},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {3},
	year = {2002},
	month = {2002/06//},
	pages = {981 - 984 vol.3 - 981 - 984 vol.3},
	abstract = {The paper proposes an algorithm for recognizing Chinese characters in many diverse fonts including Song, Fang, Kai, Hei, Yuan, Lishu, Weibei and Xingkai. The algorithm is based on features derived from peripheral direction contributions and utilizes a set of dictionaries. A 3-level matching is first performed with respect to each dictionary. The distance measures associated with these matches are then fed into a central discriminator to output the final recognition result. We propose a new multi-dictionary matching algorithm for use in the central discriminator that utilizes estimated information of neighborhood fonts. Experiments have been performed on a practical OCR software system whose recognition kernel is based on the proposed algorithm. Fast and accurate recognition has been accomplished both in title recognition, involving all of the 8 fonts, and in main-body recognition, that usually involves only the first 4 most commonly used fonts.},
	keywords = {algorithm;, central, character, Chinese, contributions;, direction, discriminator;, diverse, fonts;, image, interfaces;, LANGUAGE, MATCHING, matching;, multi-dictionary, natural, OCR, optical, peripheral, recognition, recognition;, sets;, software, system;},
	doi = {10.1109/ICIP.2002.1039139},
	author = {Wu,Xianli and M. Wu}
}
@conference {13654,
	title = {Superresolution-based enhancement of text in digital video},
	booktitle = {Pattern Recognition, 2000. Proceedings. 15th International Conference on},
	volume = {1},
	year = {2000},
	month = {2000///},
	pages = {847 -850 vol.1 - 847 -850 vol.1},
	abstract = {We present a superresolution-based text enhancement scheme to improve optical character recognition (OCR) and readability of text in digital video. The quality of video text is degraded by factors such as low resolution, antialiasing, clutter and blurring. We use the fact that the same text string often exists in consecutive frames to explore the temporal information and enhance the text image. For graphic text, we register text blocks to subpixel accuracy and fuse them to a new block with high resolution and a cleaner background. We use a projection onto convex sets based method to deblur scene text to improve readability. Experimental results show our scheme can improve OCR for graphic text and readability for scene text significantly},
	keywords = {accuracy;superresolution-based, antialiasing;blurring;clutter;digital, based, character, convex, Enhancement, enhancement;image, information;text, method;readability;subpixel, onto, recognition;, resolution;image, resolution;projection, scheme;temporal, sequences;optical, sets, string;video, text, text;geometry;image, text;low, video;graphic},
	doi = {10.1109/ICPR.2000.905546},
	author = {Huiping Li and David Doermann}
}
@conference {13612,
	title = {OCR-based rate-distortion analysis of residual coding},
	booktitle = {Image Processing, 1997. Proceedings., International Conference on},
	volume = {3},
	year = {1997},
	month = {1997/10//},
	pages = {690 -693 vol.3 - 690 -693 vol.3},
	abstract = {Symbolic compression of document images provides access to symbols found in document images and exploits the redundancy found within them. Document images are highly structured and contain large numbers of repetitive symbols. We have shown that while symbolically compressing a document image we are able to perform compressed-domain processing. Symbolic compression forms representative prototypes for symbols and encode the image by the location of these prototypes and a residual (the difference between symbol and prototype). We analyze the rate-distortion tradeoff by varying the amount of residual used in compression for both distance- and row-order coding. A measure of distortion is based on the performance of an OCR system on the resulting image. The University of Washington document database images, ground truth, and OCR evaluation software are used for experiments},
	keywords = {analysis;redundancy;representative, character, coding;distortion, coding;image, coding;lossy, coding;row-order, coding;symbolic, compression;data, compression;document, compression;lossy, database, distortion, Evaluation, image, images;document, images;experiments;ground, measure;document, OCR, of, performance;University, processing;distance-order, processing;image, prototypes;residual, recognition;rate, representation;optical, representation;progressive, software;OCR, system, theory;, transmission;rate-distortion, truth;image, Washington;compressed-domain},
	doi = {10.1109/ICIP.1997.632215},
	author = {Kia,O. E and David Doermann}
}
@conference {13571,
	title = {Image based typographic analysis of documents},
	booktitle = {Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on},
	year = {1993},
	month = {1993/10//},
	pages = {769 - 773},
	abstract = {An approach to image based typographic analysis of documents is provided. The problem requires a spatial understanding of the document layout as well as knowledge of the proper syntax. The system performs a page synthesis from the stream of formatting commands defined in a DVI file. Since the two-dimensional relationships between document components are not explicit in the page language, the authors develop a representation which preserves the two-dimensional layout, the read-order and the attributes of document components. From this hierarchical representation of the page layout we extract and analyze relevant typographic features such as margins, line and character spacing, and figure placement},
	keywords = {2D, analysis;, attributes;, based, character, commands;, component, data, description, document, DVI, extraction;, feature, figure, file;, formatting, hierarchical, image, language;, languages;, layout;, line, margins;, page, placement;, processing;, read-order;, relationships;, representation;, spacing;, spatial, structures;, syntax;, synthesis;, typographic, understanding;},
	doi = {10.1109/ICDAR.1993.395624},
	author = {David Doermann and Furuta,R.}
}
@conference {13660,
	title = {Temporal clues in handwriting},
	booktitle = {Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on},
	year = {1992},
	month = {1992/09/03/aug},
	pages = {317 - 320},
	abstract = {Handwritten character recognition is typically classified as online or offline depending on the nature of the input data. Online data consists of a temporal sequence of instrument positions while offline data is in the form of a 2D image of the writing sample. Online recognition techniques have been relatively successful but have the disadvantage of requiring the data to be gathered during the writing process. This paper presents work on the extraction of temporal information from static images of handwriting and its implications for character recognition},
	keywords = {character, extraction;, feature, handwriting, information, offline, online, recognition;, techniques;, temporal},
	doi = {10.1109/ICPR.1992.201781},
	author = {David Doermann and Rosenfeld, A.}
}
