Pub Date : 2003-08-03DOI: 10.1109/ICDAR.2003.1227831
K. K. Lau, P. Yuen, Y. Tang
It is generally agreed that an on-line recognitionsystem is always reliable than an off-line one. It is due tothe availability of the dynamic information, especially thewriting sequence of the strokes. This paper presents anew statistical method to reconstruct the writing order ofa handwritten script from a two-dimensional static image.The reconstruction process consists of two phases, namedthe training phase and the testing phase. In the trainingphase, the writing order with other attributes, such aslength and direction, are extracted from a set of trainingon-line handwritten scripts statistically to form auniversal writing model (UWM). In the testing phase,UWM is applied to reconstruct the drawing order of off-linehandwritten scripts by finding the highest totalprobability. 300 off-line signatures with ground truth areused for evaluation. Experimental results show that thereconstructed writing sequence using UWM is close to theactual writing sequence.
{"title":"Recovery of writing sequence of static images of handwriting using UWM","authors":"K. K. Lau, P. Yuen, Y. Tang","doi":"10.1109/ICDAR.2003.1227831","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227831","url":null,"abstract":"It is generally agreed that an on-line recognitionsystem is always reliable than an off-line one. It is due tothe availability of the dynamic information, especially thewriting sequence of the strokes. This paper presents anew statistical method to reconstruct the writing order ofa handwritten script from a two-dimensional static image.The reconstruction process consists of two phases, namedthe training phase and the testing phase. In the trainingphase, the writing order with other attributes, such aslength and direction, are extracted from a set of trainingon-line handwritten scripts statistically to form auniversal writing model (UWM). In the testing phase,UWM is applied to reconstruct the drawing order of off-linehandwritten scripts by finding the highest totalprobability. 300 off-line signatures with ground truth areused for evaluation. Experimental results show that thereconstructed writing sequence using UWM is close to theactual writing sequence.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130556150","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}
Pub Date : 2003-08-03DOI: 10.1109/ICDAR.2003.1227846
A. Negi, K. Shanker, C. K. Chereddi
In this paper we present a system to locate, extract andrecognize Telugu text. The circular nature of Telugu scriptis exploited for segmenting text regions using the HoughTransform. First, the Hough Transform for circles is performedon the Sobel gradient magnitude of the image tolocate text. The located circles are filled to yield text regions,followed by Recursive XY Cuts to segment the regionsinto paragraphs, lines and word regions. A regionmerging process with a bottom-up approach envelopes individualwords. Local binarization of the word MBRs yieldsconnected components containing glyphs for recognition.The recognition process first identifies candidate charactersby a zoning technique and then constructs structural featurevectors by cavity analysis. Finally, if required, crossingcount based non-linear normalization and scaling is performedbefore template matching. The segmentation processsucceeds in extracting text from images with complexNon-Manhattan layouts. The recognition process gave acharacter recognition accuracy of 97%-98%.
{"title":"Localization, extraction and recognition of text in Telugu document images","authors":"A. Negi, K. Shanker, C. K. Chereddi","doi":"10.1109/ICDAR.2003.1227846","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227846","url":null,"abstract":"In this paper we present a system to locate, extract andrecognize Telugu text. The circular nature of Telugu scriptis exploited for segmenting text regions using the HoughTransform. First, the Hough Transform for circles is performedon the Sobel gradient magnitude of the image tolocate text. The located circles are filled to yield text regions,followed by Recursive XY Cuts to segment the regionsinto paragraphs, lines and word regions. A regionmerging process with a bottom-up approach envelopes individualwords. Local binarization of the word MBRs yieldsconnected components containing glyphs for recognition.The recognition process first identifies candidate charactersby a zoning technique and then constructs structural featurevectors by cavity analysis. Finally, if required, crossingcount based non-linear normalization and scaling is performedbefore template matching. The segmentation processsucceeds in extracting text from images with complexNon-Manhattan layouts. The recognition process gave acharacter recognition accuracy of 97%-98%.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123665284","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}
Pub Date : 2003-08-03DOI: 10.1109/ICDAR.2003.1227686
N. Greco, D. Impedovo, M. Lucchese, A. Salzo, L. Sarcinella
The introduction of a new currency in Europe has changed the way of writing both the courtesy and the legal amount on checks. This paper presents the most important modifications brought on the bank-check processing system in order to solve the related problems also by proposing the software tools that must be utilized. The Computer Aided Software Engineering tools provided by the "Khoros" system are used to support the improvement of the system prototype. A visual programming environment is used to assemble the bankcheck processing system that can be easily modified and extended. The experimental results allow the adjournment of the improved system, as the modifications are introduced.
{"title":"Bank-check processing system: modifications due to the new European currency","authors":"N. Greco, D. Impedovo, M. Lucchese, A. Salzo, L. Sarcinella","doi":"10.1109/ICDAR.2003.1227686","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227686","url":null,"abstract":"The introduction of a new currency in Europe has changed the way of writing both the courtesy and the legal amount on checks. This paper presents the most important modifications brought on the bank-check processing system in order to solve the related problems also by proposing the software tools that must be utilized. The Computer Aided Software Engineering tools provided by the \"Khoros\" system are used to support the improvement of the system prototype. A visual programming environment is used to assemble the bankcheck processing system that can be easily modified and extended. The experimental results allow the adjournment of the improved system, as the modifications are introduced.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121532052","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}
Pub Date : 2003-08-03DOI: 10.1109/ICDAR.2003.1227644
M. Schambach
An important parameter for building a cursive script model is the number of different, relevant letter writing variants. An algorithm performing this task automatically by optimizing the number of letter models in an HMM-based script recognition system is presented. The algorithm iteratively modified selected letter models; for selection, quality measures like HMM distance and emission weight entropy are developed, and their correlation with recognition performance is shown. Theoretical measures for the selection of overall model complexity are presented, but best results are obtained by direct selection criteria: likelihood and recognition rate of training data. With the optimized models, an average improvement in recognition rate of up to 5.8 percent could be achieved.
{"title":"Determination of the number of writing variants with an HMM based cursive word recognition system","authors":"M. Schambach","doi":"10.1109/ICDAR.2003.1227644","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227644","url":null,"abstract":"An important parameter for building a cursive script model is the number of different, relevant letter writing variants. An algorithm performing this task automatically by optimizing the number of letter models in an HMM-based script recognition system is presented. The algorithm iteratively modified selected letter models; for selection, quality measures like HMM distance and emission weight entropy are developed, and their correlation with recognition performance is shown. Theoretical measures for the selection of overall model complexity are presented, but best results are obtained by direct selection criteria: likelihood and recognition rate of training data. With the optimized models, an average improvement in recognition rate of up to 5.8 percent could be achieved.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127721799","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}
Pub Date : 2003-08-03DOI: 10.1109/ICDAR.2003.1227653
Hiromitsu Nishimura, Takehiko Timikawa
Recognition of variously deformed character patterns is a salient subject for offline hand-printed character recognition. Sufficient recognition performance for practical use has not been achieved despite reports of many recognition techniques. Our research examines effective recognition techniques for deformed characters, extending conventional recognition techniques using online character writing information containing writing pressure data. This study extends conventional recognition techniques using online character writing information containing writing pressure information. A recognition system using simple pattern matching and HMM was made for evaluation experiments using common hand-printed English character patterns from the ETL6 database to determine effectiveness of the proposed extending recognition method. Character recognition performance is increased in both expansion recognition methods using online writing information.
{"title":"Offline character recognition using online character writing information","authors":"Hiromitsu Nishimura, Takehiko Timikawa","doi":"10.1109/ICDAR.2003.1227653","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227653","url":null,"abstract":"Recognition of variously deformed character patterns is a salient subject for offline hand-printed character recognition. Sufficient recognition performance for practical use has not been achieved despite reports of many recognition techniques. Our research examines effective recognition techniques for deformed characters, extending conventional recognition techniques using online character writing information containing writing pressure data. This study extends conventional recognition techniques using online character writing information containing writing pressure information. A recognition system using simple pattern matching and HMM was made for evaluation experiments using common hand-printed English character patterns from the ETL6 database to determine effectiveness of the proposed extending recognition method. Character recognition performance is increased in both expansion recognition methods using online writing information.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127791463","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}
Pub Date : 2003-08-03DOI: 10.1109/ICDAR.2003.1227856
S. Touj, N. Amara, H. Amiri
The Generalized Hough Transform is a technique used todetect arbitrary objects in a given image. This techniqueis known for its capacity of absorption of distortions aswell as noises. In the present paper, we describe anapproach showing the efficiency of the use of theGeneralized Hough Transform to recognize Arabicprinted characters in their different shapes.
{"title":"Generalized hough transform for arabic optical character recognition","authors":"S. Touj, N. Amara, H. Amiri","doi":"10.1109/ICDAR.2003.1227856","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227856","url":null,"abstract":"The Generalized Hough Transform is a technique used todetect arbitrary objects in a given image. This techniqueis known for its capacity of absorption of distortions aswell as noises. In the present paper, we describe anapproach showing the efficiency of the use of theGeneralized Hough Transform to recognize Arabicprinted characters in their different shapes.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128090028","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}
Pub Date : 2003-08-03DOI: 10.1109/ICDAR.2003.1227631
Zhenlong Bai, Qiang Huo
In this paper, we present a new approach to extracting the target text line from a document image captured by a pen scanner. Given the binary image, a set of possible text lines are first formed by nearest-neighbor grouping of connected components (CC). They are then refined by text line merging and adding the missed CCs. The possible target text line is identified by using a geometric feature based score function and fed to an OCR engine for character recognition. If the recognition result is confident enough, the target text line is accepted. Otherwise, all the remaining text lines are fed to the OCR engine to verify whether an alternative target text line exists or the whole image should be rejected. The effectiveness of the above approach is confirmed by experiments on a testing database consisting of 117 document images captured by C-Pen and ScanEye pen scanners.
{"title":"An approach to extracting the target text line from a document image captured by a pen scanner","authors":"Zhenlong Bai, Qiang Huo","doi":"10.1109/ICDAR.2003.1227631","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227631","url":null,"abstract":"In this paper, we present a new approach to extracting the target text line from a document image captured by a pen scanner. Given the binary image, a set of possible text lines are first formed by nearest-neighbor grouping of connected components (CC). They are then refined by text line merging and adding the missed CCs. The possible target text line is identified by using a geometric feature based score function and fed to an OCR engine for character recognition. If the recognition result is confident enough, the target text line is accepted. Otherwise, all the remaining text lines are fed to the OCR engine to verify whether an alternative target text line exists or the whole image should be rejected. The effectiveness of the above approach is confirmed by experiments on a testing database consisting of 117 document images captured by C-Pen and ScanEye pen scanners.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130036968","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}
Pub Date : 2003-08-03DOI: 10.1109/ICDAR.2003.1227758
H. Hase, Toshiyuki Shinokawa, M. Yoneda, C. Suen
In this paper, we present a method of recognizinginclined, rotated characters. First we construct an eigensub-space for each category using the covariance matrixwhich is calculated from a sufficient number of rotatedcharacters. Next, we can obtain a locus by projectingtheir rotated characters onto the eigen sub-space andinterpolating between their projected points. An unknowncharacter is also projected onto the eigen sub-space ofeach category. Then, the verification is carried out bycalculating the distance between the projected point ofthe unknown character and the locus. In our experiment,we obtained quite good results for the CENTURY font of26 capital letters of the English alphabet (A, B, .... ,Z).This method has the added advantage of obtaining therecognition result (category) and angle of inclination atthe same time
{"title":"Recognition of rotated characters by Eigen-space","authors":"H. Hase, Toshiyuki Shinokawa, M. Yoneda, C. Suen","doi":"10.1109/ICDAR.2003.1227758","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227758","url":null,"abstract":"In this paper, we present a method of recognizinginclined, rotated characters. First we construct an eigensub-space for each category using the covariance matrixwhich is calculated from a sufficient number of rotatedcharacters. Next, we can obtain a locus by projectingtheir rotated characters onto the eigen sub-space andinterpolating between their projected points. An unknowncharacter is also projected onto the eigen sub-space ofeach category. Then, the verification is carried out bycalculating the distance between the projected point ofthe unknown character and the locus. In our experiment,we obtained quite good results for the CENTURY font of26 capital letters of the English alphabet (A, B, .... ,Z).This method has the added advantage of obtaining therecognition result (category) and angle of inclination atthe same time","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130180903","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}
This paper deals with an Optical Character Recognitionsystem for printed Urdu, a popular Indian script. Thedevelopment of OCR for this script is difficult because (i) alarge number of characters have to be recognized (ii) thereare many similar shaped characters. In the proposedsystem individual characters are recognized using acombination of topological, contour and water reservoirconcept based features. The feature detection methods aresimple and robust. A prototype of the system has beentested on printed Urdu characters and currently achieves97.8% character level accuracy on average.
{"title":"Recognition of printed Urdu script","authors":"Zaheer Ahmad, Jehanzeb Khan Orakzai, Inam Shamsher","doi":"10.1109/ICDAR.2003.1227844","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227844","url":null,"abstract":"This paper deals with an Optical Character Recognitionsystem for printed Urdu, a popular Indian script. Thedevelopment of OCR for this script is difficult because (i) alarge number of characters have to be recognized (ii) thereare many similar shaped characters. In the proposedsystem individual characters are recognized using acombination of topological, contour and water reservoirconcept based features. The feature detection methods aresimple and robust. A prototype of the system has beentested on printed Urdu characters and currently achieves97.8% character level accuracy on average.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130277437","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}
Pub Date : 2003-08-03DOI: 10.1109/ICDAR.2003.1227748
Luiz Oliveira, R. Sabourin, Flávio Bortolozzi, C. Suen
Feature selection for ensembles has shown to be an effectivestrategy for ensemble creation. In this paper we presentan ensemble feature selection approach based on a hierarchicalmulti-objective genetic algorithm. The first level performsfeature selection in order to generate a set of goodclassifiers while the second one combines them to providea set of powerful ensembles. The proposed method is evaluatedin the context of handwritten digit recognition, usingthree different feature sets and neural networks (MLP) asclassifiers. Experiments conducted on NIST SD19 demonstratedthe effectiveness of the proposed strategy.
{"title":"Feature selection for ensembles:a hierarchical multi-objective genetic algorithm approach","authors":"Luiz Oliveira, R. Sabourin, Flávio Bortolozzi, C. Suen","doi":"10.1109/ICDAR.2003.1227748","DOIUrl":"https://doi.org/10.1109/ICDAR.2003.1227748","url":null,"abstract":"Feature selection for ensembles has shown to be an effectivestrategy for ensemble creation. In this paper we presentan ensemble feature selection approach based on a hierarchicalmulti-objective genetic algorithm. The first level performsfeature selection in order to generate a set of goodclassifiers while the second one combines them to providea set of powerful ensembles. The proposed method is evaluatedin the context of handwritten digit recognition, usingthree different feature sets and neural networks (MLP) asclassifiers. Experiments conducted on NIST SD19 demonstratedthe effectiveness of the proposed strategy.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134054414","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}
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