2010 12th International Conference on Frontiers in Handwriting Recognition最新文献

{"title":"Efficient Transcript Mapping to Ease the Creation of Document Image Segmentation Ground Truth with Text-Image Alignment","authors":"N. Stamatopoulos, G. Louloudis, B. Gatos","doi":"10.1109/ICFHR.2010.43","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.43","url":null,"abstract":"One of the major issues in document image processing is the efficient creation of ground truth in order to be used for training and evaluation purposes. Since a large number of tools have to be trained and evaluated in realistic circumstances, we need to have a quick and low cost way to create the corresponding ground truth. Moreover, the specific need for having the correct text correlated with the corresponding image area in text line and word level makes the process of ground truth creation a difficult, tedious and costly task. In this paper, we introduce an efficient transcript mapping technique to ease the construction of document image segmentation ground truth that includes text-image alignment. The proposed text line transcript mapping technique is based on Hough transform that is guided by the number of the text lines. Concerning the word segmentation ground truth, a gap classification technique constrained by the number of the words is used. Experimental results prove that using the proposed technique for handwritten documents, the percentage of time saved for ground truth creation and text-image alignment is more than 90%.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130988055","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":"DooDB: A Graphical Password Database Containing Doodles and Pseudo-Signatures","authors":"M. Martinez-Diaz, Julian Fierrez, C. Martin-Diaz, J. Ortega-Garcia","doi":"10.1109/ICFHR.2010.59","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.59","url":null,"abstract":"Touch screen-enabled devices are proliferating in the communications and entertainment markets. In this scenario, the use of graphical passwords for user validation is receiving an increasing interest in the last years. Unlike in other fields of research on automatic user authentication, such as biometrics, there are no public databases of graphical passwords usable for research purposes (to the extent of our knowledge). In the present work, the recently captured DooDB database is introduced. This database comprises two sub corpora: doodles and simplified signatures (pseudo-signatures). These data were produced by 100 users, who were asked to draw with their fingertips over a mobile device touch screen. Forgeries are also included in the database. A quantitative analysis of both datasets is first performed. Preliminary verification experiments using the two kinds of graphical passwords are reported.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130454796","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":"Hand-Drawn Symbol Spotting Using Semi-definite Programming Based Sub-graph Matching","authors":"K. Bhuvanagiri, Aditya Vikram Daga, R. Sitaram, Suryaprakash Kompalli","doi":"10.1109/ICFHR.2010.51","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.51","url":null,"abstract":"In this paper we address the problem of hand-drawn symbol spotting in document images. We use stochastic graphical models (SGMs) to represent the structure and variations of hand-drawn symbols. We use a framework which first carries out segmentation and graph formation of the input image, followed by sub-graph matching for spotting of hand-drawn symbols. We used SGMs in place of sub-graphs in a semi-definite programming based sub-graph matching to do the spotting. The experimental results validate our framework. We were able to spot hand-drawn symbols from 10 classes with 78.89% accuracy in a database of 76 document images and also were able to deal with confusingly similar symbol classes.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123323086","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":"The Problem of Handwritten Mathematical Expression Recognition Evaluation","authors":"Ahmad Montaser Awal, H. Mouchère, C. Viard-Gaudin","doi":"10.1109/ICFHR.2010.106","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.106","url":null,"abstract":"We discuss in this paper some issues related to the problem of mathematical expression recognition. The very first important issue is to define how to ground truth a dataset of handwritten mathematical expressions, and next we have to face the problem of benchmarking systems. We propose to define some indicators and the way to compute them so as they reflect the actual performances of a given system.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123055056","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":"Using Field Interdependence to Improve Correction Performance in a Transducer-Based OCR Post-Processing System","authors":"J. Pérez-Cortes, R. Llobet, J. Navarro-Cerdán, J. Arlandis","doi":"10.1109/ICFHR.2010.99","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.99","url":null,"abstract":"In an automatic handwritten form processing system it is often necessary to use the lexical or linguistic restrictions present in the field contents in order to obtain acceptable recognition rates. Since each field is known to hold a given kind of information (name, address...), a language model can be defined for it. But, often, in a typical form there are fields linked by known relations, like “Street” and “Postal Code” or “Country” and “City”. We have used Weighted Finite-State Transducers (WFSTs) to combine Stochastic Error-Correcting Language Models from different interdependent fields in real handwritten forms and measured the improvements obtained.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"320 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132249052","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":"H-DIBCO 2010 - Handwritten Document Image Binarization Competition","authors":"I. Pratikakis, B. Gatos, K. Ntirogiannis","doi":"10.1109/ICFHR.2010.118","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.118","url":null,"abstract":"H-DIBCO 2010 is the International Document Image Binarization Contest which is dedicated to handwritten document images organized in conjunction with ICFHR 2010 conference. The general objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures. This paper reports on the contest details including the evaluation measures used as well as the performance of the 17 submitted methods along with a short description of each method.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134259258","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":"A New Approach for Synthesis and Recognition of Large Scale Handwritten Chinese Words","authors":"Gang Liu, Lianwen Jin, Kai Ding, Hanyu Yan","doi":"10.1109/ICFHR.2010.94","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.94","url":null,"abstract":"Lacking of dataset is still a serious problem for researchers who study on online handwriting word recognition (HWR). In this paper, a handwritten Chinese word synthesis method is proposed for the first time to generate a large scale handwritten Chinese word dataset. The distributions of shape and position characteristics, such as aspect radio, character interval and the angle of gravity center line in each word sample of the Word8888 dataset have been estimated respectively. Based on this, we synthesize as large as 44,208 categories of 8,311,104 unconstrained handwritten Chinese word samples. To verify the validity of the synthesized dataset, a practical rotation free handwriting Chinese word recognition system is presented based on a new holistic approach. Experimental results for randomly rotated word samples demonstrate that the holistic approach can achieve 91.96% recognition accuracy, which provides evidence for the effectiveness of our method.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115756933","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":"Contribution of Ancient Indians to 'Writing' (With Special Emphasis on South Asian and Indian Writing Systems)","authors":"M. Lakshmithathachar","doi":"10.1109/ICFHR.2010.130","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.130","url":null,"abstract":"Summary form only given. Many models have been proposed over the years to study human movements in general and handwriting in particular: models relying on neural networks, dynamics models, psychophysical models, kinematic models and models exploiting minimization principles. Among the models that can be used to provide analytical representations of a pen stroke, the Kinematic Theory of rapid human movements and its delta-lognormal model has often served as a guide in the design of pattern recognition systems relying on the exploitation of the fine neuromotricity, like on-line handwriting recognition, signature verification as well as in the design of intelligent systems involving in a way or another, the global processing of human movements. Among other things, this invited lecture aims at elaborating a theoretical background for many handwriting applications as well as providing some basic knowledge that could be integrated or taking care of in the development of automatic pattern recognition systems. More specifically, we will overview the basic neuromotor properties of single strokes and explain how they can be superimposed vectorially to generate complex pen tip trajectories. Doing so, we will report on various projects conducted by our team and our collaborators. First, we will present a brief comparative survey of the different models in the field and focus on the family of models involving lognormal functions. Then, from a practical perspective, we will describe two new parameter extraction algorithms suitable for the reverse engineering of individual strokes as well as of complex handwriting signals. We will show how the resulting representation could be employed to characterize signers and writers and how the corresponding feature sets could be exploited to study the effects of various factors, like aging and health problems, on handwriting variability. We will also describe some methodologies to generate automatically huge on-line handwriting databases for either writer dependent or writer independent applications as well as for the production of synthetic signature databases. From a theoretical perspective, we will explain how, using an original psychophysical set up, we have been able to validate the basic hypothesis of the Kinematic Theory and to test its most distinctive predictions. We will complete this survey by explaining how the Kinematic Theory could be utilized to improve electromyographic and electroencephalographic signal processing, opening a window on novel potential applications for on-line handwriting processing, particularly in biomedical engineering and in some fields of the neurosciences.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131537228","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":"Recognition of Words from Legal Amounts of Indian Bank Cheques","authors":"R. Jayadevan, U. Pal, F. Kimura","doi":"10.1109/ICFHR.2010.33","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.33","url":null,"abstract":"Legal amount of Indian bank cheques contains 36 different words. Most of the Indian cheques in cities are written in English although some of them are written in Hindi and other state languages. As the legal amount words written in English can be case sensitive, the size of the lexicon for legal word recognition can go up to 108 (3´36). In this paper a lexicon driven segmentation-recognition scheme is proposed for the recognition of legal amount words from Indian bank cheques written in English. A water reservoir concept is used to pre-segment the words into primitive components and the primitive components of a word are then merged into possible characters to get the best word using the lexicon of 36 different legal words of bank cheque. To merge these primitive components into characters and to get optimum character segmentation, dynamic programming is employed using total likelihood of the characters of a word as an objective function. To calculate the likelihood of a character, Modified Quadratic Discriminant Function (MQDF) is used. The features used in the MQDF are mainly based on directional features of the contour points of the components. In the paper it is assumed that the words are already extracted from the cheque image for recognition. A database consisting of 5400 words, collected from 50 writers has been used for testing the system and an accuracy of 97.04% was observed.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114560816","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":"The Zone-Based Projection Distance Feature Extraction Method for Handwritten Numeral/Mixed Numerals Recognition of Indian Scripts","authors":"S. Rajashekararadhya, P. Ranjan","doi":"10.1109/ICFHR.2010.101","DOIUrl":"https://doi.org/10.1109/ICFHR.2010.101","url":null,"abstract":"Handwriting recognition has always been a challenging task in image processing and pattern recognition. India is a multi-lingual, multi-script country, where eighteen official scripts are accepted and there are over a hundred regional languages. The feature extraction method is probably the most effective method in achieving high recognition performance. In this study we proposed a zone-based feature extraction algorithm scheme for the recognition of off-line handwritten numerals of south-Indian scripts. The character centroid is computed and the character/numeral image (50×50) is further divided in to 25 equal zones (10×10). The average distance from the character centroid to the pixels present in the zone column was computed. This procedure was sequentially repeated for all the zone/grid/box columns present in the zone (10 features). This procedure was sequentially repeated for the entire zone present in the numeral image (250 features). Similarly, again the character centroid was computed and the image is further divided into 50 equal zones (5×10). The average distance from the image centroid to the pixels present in the zone was computed. This procedure was sequentially repeated for the entire zone present in the numeral image (50 features). There could be some zone/zone column that is empty of foreground pixels, then the feature value of that zone column/zone in the feature vector is zero. Finally, 300 such features were extracted for classification and recognition. The nearest neighbor, feed forward back propagation neural network and support vector machine classifiers were used for subsequent classification and recognition purposes. We obtained a recognition rate of 98.05, for Kannada numerals, 95.1 for Tamil numerals, 97.2 for Telugu numerals and 95.7 for Malayalam numerals using support vector machine.","PeriodicalId":335044,"journal":{"name":"2010 12th International Conference on Frontiers in Handwriting Recognition","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114584315","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}