{"title":"ICDAR 2009 Book Structure Extraction Competition","authors":"A. Doucet, G. Kazai, J. Meunier","doi":"10.1109/ICDAR.2011.298","DOIUrl":"https://doi.org/10.1109/ICDAR.2011.298","url":null,"abstract":"This paper introduces the Book Structure Extraction competition run at ICDAR 2009. The goal of the competition is to evaluate and compare automatic techniques for deriving structure information from digitized books, which could then be used to aid navigation inside the books. More specifically, the task that participants are faced with is to construct hyperlinked tables of contents for a collection of 1,000 digitized books. This paper describes the setup of the competition, the book collection used in the task, and the proposed measures for the evaluation. Results of the evaluation will be presented at the ICDAR 2009 conference and will be published in the INEX 2009 proceedings.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123396230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Handwritten Text Line Identification in Indian Scripts","authors":"B. Chaudhuri, Sumedha Bera","doi":"10.1109/ICDAR.2009.69","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.69","url":null,"abstract":"Preprocessing in handwritten text OCR involves line, word and character segmentation. This paper deals with text line identification of handwritten Indian scripts, especially of Bangla, as well as English, Hindi, Malayalam, etc. Here, a new dual method based on interdependency between text-line and inter-line gap is proposed. The method draws curves simultaneously through the text and inter-line gap points found from strip-wise histogram peaks and inter-peak valleys. The curves start from left and move right while one type of points guides the curve of other type so that the curves do not intersect. Then these curves are allowed to iteratively evolve so that the text-line curves cross more character strokes while inter-line curves cross less character strokes and yet keep the curves as straight as possible. After several iterations, the curves stabilize and define the final text-lines and inter-line gaps. The approach works well on text of different scripts with various geometric layouts, including poetry.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"418 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124253900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Restoration and Segmentation of Highly Degraded Characters Using a Shape-Independent Level Set Approach and Multi-level Classifiers","authors":"R. F. Moghaddam, David Rivest-Hénault, M. Cheriet","doi":"10.1109/ICDAR.2009.107","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.107","url":null,"abstract":"Segmentation of ancient documents is challenging. In the worst cases, text characters become fragmented as the results of strong degradation processes. New active contour methods allow to handle difficult cases in a spatially coherent fashion. However, most of those method use a restrictive, a priori shape information that limit their application. In this work, we propose to address this issue by combining two complementary approaches. First, multi-level classifiers, which take advantage of the stroke width a priori information, allow to locate candidate character pixels. Second, a level set active contour scheme is used to identify the boundary of a character. Tests have been conducted on a set of ancient degraded Hebraic character images. Numerical results are promising.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127085488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
María José Castro Bleda, Salvador España Boquera, J. Gorbe-Moya, Francisco Zamora-Martínez, D. Llorens, A. Marzal, F. Prat, J. M. Vilar
{"title":"Improving a DTW-Based Recognition Engine for On-line Handwritten Characters by Using MLPs","authors":"María José Castro Bleda, Salvador España Boquera, J. Gorbe-Moya, Francisco Zamora-Martínez, D. Llorens, A. Marzal, F. Prat, J. M. Vilar","doi":"10.1109/ICDAR.2009.209","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.209","url":null,"abstract":"Our open source real-time recognition engine for on-line isolated handwritten characters is a 3-Nearest Neighbor classifier that uses approximate dynamic time warping comparisons with a set of prototypes filtered by two fast distance-based methods. This engine achieved excellent classification rates on two writer-independent tasks:UJIpenchars and Pendigits. We present the integration of multilayer perceptrons into our engine, an improvement that speeds up the recognition process by taking advantage of the independence of these networks’ classification times from training set sizes. We also present experimental results on our new publicly available UJIpenchars2 database and on Pendigits.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127276693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Honggang Zhang, Jun Guo, Guang Chen, Chun-Guang Li
{"title":"HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition","authors":"Honggang Zhang, Jun Guo, Guang Chen, Chun-Guang Li","doi":"10.1109/ICDAR.2009.15","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.15","url":null,"abstract":"In this paper, we present a large scale off-line handwritten Chinese character database-HCL2000 which will be made public available for the research community. The database contains 3,755 frequently used simplified Chinesecharacters written by 1,000 different subjects. The writers’ information is incorporated in the database to facilitate testing on grouping writers with different background such as age, occupation, gender, and education etc. We investigate some characteristics of writing styles from different groups of writers. We evaluate HCL2000 database using three different algorithms as a baseline. We decide to publish the database along with this paper and make it free for a research purpose.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123568699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. T. Ibrahim, M. Kyan, M. A. Khan, K. Alimgeer, L. Guan
{"title":"On-Line Signature Verification: Directional Analysis of a Signature Using Weighted Relative Angle Partitions for Exploitation of Inter-Feature Dependencies","authors":"M. T. Ibrahim, M. Kyan, M. A. Khan, K. Alimgeer, L. Guan","doi":"10.1109/ICDAR.2009.113","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.113","url":null,"abstract":"In this paper, we propose a new directional analysis tool for On-line signatures that decomposes the given input signature into directional bands on the basis of relative angles. Our directional analysis tool takes the independent trajectories (horizontal and vertical) as an input and then decomposes them into directional bands on the basis of relative angles. We have used both user-dependent and user-independent thresholds for selecting an optimal number of partitions for each signer. By decomposing signature trajectories based upon relative angles of an individual’s signature, the resulting process can be thought of as one that exploits inter-feature dependencies . In the verification phase, distances of each partitioned trajectory of a test signature are calculated against a similarly partitioned template trajectory for a known signer. Each partition is then weighted based on its quality and quantity. Experimental results demonstrate the superiority of our approach to On-line signature verification in comparison with other techniques.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126905995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Natarajan, Krishna Subramanian, Anurag Bhardwaj, R. Prasad
{"title":"Stochastic Segment Modeling for Offline Handwriting Recognition","authors":"P. Natarajan, Krishna Subramanian, Anurag Bhardwaj, R. Prasad","doi":"10.1109/ICDAR.2009.278","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.278","url":null,"abstract":"In this paper, we present a novel approach for incorporating structural information into the hidden Markov Modeling (HMM) framework for offline handwriting recognition. Traditionally, structural features have been used in recognition approaches that rely on accurate segmentation of words into smaller units (sub-words or characters). However, such segmentation based approaches do not perform well on real-world handwritten images, because breaks and merges in glyphs typically create new connected components that are not observed in the training data. To mitigate the problem of having to derive accurate segmentation from connected components, we present a novel framework where the HMM based recognition system trained on shorter-span features is used to generate the 2-D character images (the “Stochastic Segments”), and then another classifier that uses structural features extracted from the stochastic character segments generates a new set of scores. Finally, the scores from the HMM system and from structural matching are used in combination to generate a hypothesis that is better than the results from either the HMM or from structural matching alone. We demonstrate the efficacy of our approach by reporting experimental results on a large corpus of handwritten Arabic documents.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122637267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Walaa Aly, S. Uchida, Akio Fujiyoshi, Masakazu Suzuki
{"title":"Statistical Classification of Spatial Relationships among Mathematical Symbols","authors":"Walaa Aly, S. Uchida, Akio Fujiyoshi, Masakazu Suzuki","doi":"10.1109/ICDAR.2009.90","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.90","url":null,"abstract":"In this paper, a statistical decision method for automatic classification of spatial relationships between each adjacent pair is proposed. Each pair is composed of mathematical symbols and/or alphabetical characters. Special treatment of mathematical symbols with variable size is important.This classification is important to recognize an accurate structure analysis module of math OCR. Experimental results on a very large database showed that the proposed method worked well with an accuracy of 99.57% by two important geometric feature relative size and relative position.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129475758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prototype Selection for Handwritten Connected Digits Classification","authors":"C. S. Pereira, George D. C. Cavalcanti","doi":"10.1109/ICDAR.2009.186","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.186","url":null,"abstract":"After the handwritten segmentation process, it is common to have connected digits. This is due to the great size and shape digit variations. In addition, the acquisition and the binarization processes can add noise to the images. These under segmented images, when given as input to classifiers which are specialists to deal with digits separately, should lead to errors. Aiming to detect the handwritten connected digits, it is herein introduced a hybrid system architecture to be used as a segmentation pos-processing task. The proposed system is based on a prototype selection scheme that combines self-generating prototypes and Gaussian mixtures. Besides, this work presents a set of features for the proposed problem. A real-world database of handwritten digits was used to validate the new approach. The results obtained in the experimental study showed that the hybrid strategy achieved promising accuracy rates.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128459361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hierarchical Decomposition of Handwriting Deformation Vector Field Using 2D Warping and Global/Local Affine Transformation","authors":"T. Wakahara, S. Uchida","doi":"10.1109/ICDAR.2009.33","DOIUrl":"https://doi.org/10.1109/ICDAR.2009.33","url":null,"abstract":"This paper addresses the basic problem of how to extract, describe, and evaluate handwriting deformation from not the statistical but the deterministic viewpoint. The key ideas are threefold. The first idea is to apply 2D warping to extraction of handwriting deformation vector field (DVF) between a pair of input and target images. The second idea is to hierarchically decompose the DVF by a parametric deformation model of global/local affine transformation. As a result, the DVF is expressed by a series of deformation components each of which is characterized by a window size of local affine transformation. The third idea is interrupting of the series of deformation components to obtain natural, reasonable handwriting deformation. Experiments using the handwritten numeral database IPTP CDROM1B show that 31.1% of the handwriting DVF is expressed by global affine transformation, and the subsequent few local affine transformations successfully discriminate natural handwriting deformation from unnatural one.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128732957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}