This paper presents three evaluation criteria's for a comparison of two characters segmentation methods for handwritten Arabic words. The first segmentation method is based on a combination between the projection and the minima and maxima of the contour of the image. The second method is a combination between Hough Transform (HT) and Mathematical Morphology (MM) operators. These methods are developed, evaluated and compared with reference to IFN/ENIT-database in comparison of three evaluation criteria's. The first criterion is based on the segments positions (SP). The second criterion is based on the segments numbers (SN). The third is based on the recognition rates by Transparent Neural Network (RR).
{"title":"Three Evaluation Criteria's towards a Comparison of Two Characters Segmentation Methods for Handwritten Arabic Script","authors":"F. B. Samoud, S. Maddouri, H. Amiri","doi":"10.1109/ICFHR.2012.283","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.283","url":null,"abstract":"This paper presents three evaluation criteria's for a comparison of two characters segmentation methods for handwritten Arabic words. The first segmentation method is based on a combination between the projection and the minima and maxima of the contour of the image. The second method is a combination between Hough Transform (HT) and Mathematical Morphology (MM) operators. These methods are developed, evaluated and compared with reference to IFN/ENIT-database in comparison of three evaluation criteria's. The first criterion is based on the segments positions (SP). The second criterion is based on the segments numbers (SN). The third is based on the recognition rates by Transparent Neural Network (RR).","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126565944","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}
In this paper, orthogonal polynomials series are used to approximate the time functions associated to the signatures. The coefficients in these series expansions, computed resorting to least squares estimation techniques, are then used as features to model the signatures. Different combinations of several time functions (pen coordinates, incremental variation of pen coordinates and pen pressure), related to the signing process, are analyzed in this paper for two different signature styles, namely, Western signatures and Chinese signatures of a publicly available Signature Database. Two state-of-the-art classification methods, namely, Support Vector Machines and Random Forests are used in the verification experiments. The proposed online signature verification system delivers error rates comparable to results reported over the same signature datasets in a previous signature verification competition.
{"title":"Online Signature Verification Based on Legendre Series Representation: Robustness Assessment of Different Feature Combinations","authors":"Marianela Parodi, J. Gómez, M. Liwicki","doi":"10.1109/ICFHR.2012.251","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.251","url":null,"abstract":"In this paper, orthogonal polynomials series are used to approximate the time functions associated to the signatures. The coefficients in these series expansions, computed resorting to least squares estimation techniques, are then used as features to model the signatures. Different combinations of several time functions (pen coordinates, incremental variation of pen coordinates and pen pressure), related to the signing process, are analyzed in this paper for two different signature styles, namely, Western signatures and Chinese signatures of a publicly available Signature Database. Two state-of-the-art classification methods, namely, Support Vector Machines and Random Forests are used in the verification experiments. The proposed online signature verification system delivers error rates comparable to results reported over the same signature datasets in a previous signature verification competition.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126189982","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}
Broken or partially visible characters is common phenomenon in historical documents. It stems from various factors, such as overlaid text or degradation. Restoring such characters is necessary for document analysis applications. This paper presents a new approach for restoring underlaying Hebrew broken characters that were partially occluded by Arabic text in a palimpsest. We apply text recognition on the fragments of the Hebrew letters and select the k candidate letters that match best the fragments of the Hebrew letters. We then complete the broken Hebrew characters using active contour with the k candidates as shape priors. We use a modified geodesic active contour, which we tailored to occluded text restoration. It is initialized on the fragments of the Hebrew text, then it undergoes an expansion phase and a contraction phase via the occluding Arabic text to form the restored Hebrew character. We measure the distance between the completed character and its corresponding priors and choose the shape with the minimal distance as the reconstructed character. Experimental results are presented. On average 68% of the characters were correctly reconstructed.
{"title":"Occluded Character Restoration Using Active Contour with Shape Priors","authors":"Rafi Cohen, K. Kedem, I. Dinstein, Jihad El-Sana","doi":"10.1109/ICFHR.2012.243","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.243","url":null,"abstract":"Broken or partially visible characters is common phenomenon in historical documents. It stems from various factors, such as overlaid text or degradation. Restoring such characters is necessary for document analysis applications. This paper presents a new approach for restoring underlaying Hebrew broken characters that were partially occluded by Arabic text in a palimpsest. We apply text recognition on the fragments of the Hebrew letters and select the k candidate letters that match best the fragments of the Hebrew letters. We then complete the broken Hebrew characters using active contour with the k candidates as shape priors. We use a modified geodesic active contour, which we tailored to occluded text restoration. It is initialized on the fragments of the Hebrew text, then it undergoes an expansion phase and a contraction phase via the occluding Arabic text to form the restored Hebrew character. We measure the distance between the completed character and its corresponding priors and choose the shape with the minimal distance as the reconstructed character. Experimental results are presented. On average 68% of the characters were correctly reconstructed.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122422050","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}
Jin Chen, Bing Zhang, Huaigu Cao, R. Prasad, P. Natarajan
Feature extraction is an important step in off-line handwriting recognition systems to represent raw handwriting in a low-dimensional, tractable feature space. Traditionally, linear feature transforms such as Principle Component Analysis (PCA), Linear Discriminative Analysis (LDA) are commonly used. The assumptions they make, however, usually cannot be satisfied in practice and thus the best performance is not obtained. In this paper, we apply the Region-Dependent non-linear feature Transform (RDT) to handwriting recognition. RDT is one type of non-linear feature transforms which captures the discriminating power much better than traditional linear ones. We justify the effectiveness of RDT on handwriting features using an HMM-based handwriting recognition system on an Arabic handwriting dataset, which consists of 38K pages of handwriting, over 3M handwritten words. Experimental results show that RDT is able to decrease the word error rates (WERs) relatively by 4% to 7% with statistical significance, comparing to two LDA-based baseline systems.
{"title":"Applying Discriminatively Optimized Feature Transform for HMM-based Off-Line Handwriting Recognition","authors":"Jin Chen, Bing Zhang, Huaigu Cao, R. Prasad, P. Natarajan","doi":"10.1109/ICFHR.2012.182","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.182","url":null,"abstract":"Feature extraction is an important step in off-line handwriting recognition systems to represent raw handwriting in a low-dimensional, tractable feature space. Traditionally, linear feature transforms such as Principle Component Analysis (PCA), Linear Discriminative Analysis (LDA) are commonly used. The assumptions they make, however, usually cannot be satisfied in practice and thus the best performance is not obtained. In this paper, we apply the Region-Dependent non-linear feature Transform (RDT) to handwriting recognition. RDT is one type of non-linear feature transforms which captures the discriminating power much better than traditional linear ones. We justify the effectiveness of RDT on handwriting features using an HMM-based handwriting recognition system on an Arabic handwriting dataset, which consists of 38K pages of handwriting, over 3M handwritten words. Experimental results show that RDT is able to decrease the word error rates (WERs) relatively by 4% to 7% with statistical significance, comparing to two LDA-based baseline systems.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121372524","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}
H-DIBCO 2012 is the International Document Image Binarization Competition which is dedicated to handwritten document images organized in conjunction with ICFHR 2012 conference. The 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 24 submitted methods along with a short description of each method.
{"title":"ICFHR 2012 Competition on Handwritten Document Image Binarization (H-DIBCO 2012)","authors":"I. Pratikakis, B. Gatos, K. Ntirogiannis","doi":"10.1109/ICFHR.2012.216","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.216","url":null,"abstract":"H-DIBCO 2012 is the International Document Image Binarization Competition which is dedicated to handwritten document images organized in conjunction with ICFHR 2012 conference. The 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 24 submitted methods along with a short description of each method.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115764652","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}
Digital ink curves are typically represented as series of points sampled at certain time intervals. We are interested in the problem of how to select a minimal subset of sample points to approximate a digital ink curve within a given error bound. We present an algorithm to find an approximation with a specified number of points and providing the minimum cumulative error. Alternatively, it may be used to select the minimum number of points required to satisfy an error bound. The method uses dynamic programming and has a cost linear in the number of points.
{"title":"Optimization of Point Selection on Digital Ink Curves","authors":"Rui Hu, S. Watt","doi":"10.1109/ICFHR.2012.252","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.252","url":null,"abstract":"Digital ink curves are typically represented as series of points sampled at certain time intervals. We are interested in the problem of how to select a minimal subset of sample points to approximate a digital ink curve within a given error bound. We present an algorithm to find an approximation with a specified number of points and providing the minimum cumulative error. Alternatively, it may be used to select the minimum number of points required to satisfy an error bound. The method uses dynamic programming and has a cost linear in the number of points.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116227910","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}
V. Papavassiliou, Fotini Simistira, V. Katsouros, G. Carayannis
Document image binarization is an initial though critical stage towards the recognition of the text components of a document. This paper describes an efficient method based on mathematical morphology for extracting text regions from degraded handwritten document images. The basic stages of our approach are: (a) top-hat-by-reconstruction to produce a filtered image with reasonable even background, (b) region growing starting from a set of seed points and attaching to each seed similar intensity neighboring pixels and (c) conditional extension of the initially detected text regions based on the values of the second derivative of the filtered image. The method was evaluated on the benchmarking dataset of the International Document Image Binarization Contest (DIBCO 2011) and show promising results.
{"title":"A Morphology Based Approach for Binarization of Handwritten Documents","authors":"V. Papavassiliou, Fotini Simistira, V. Katsouros, G. Carayannis","doi":"10.1109/ICFHR.2012.158","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.158","url":null,"abstract":"Document image binarization is an initial though critical stage towards the recognition of the text components of a document. This paper describes an efficient method based on mathematical morphology for extracting text regions from degraded handwritten document images. The basic stages of our approach are: (a) top-hat-by-reconstruction to produce a filtered image with reasonable even background, (b) region growing starting from a set of seed points and attaching to each seed similar intensity neighboring pixels and (c) conditional extension of the initially detected text regions based on the values of the second derivative of the filtered image. The method was evaluated on the benchmarking dataset of the International Document Image Binarization Contest (DIBCO 2011) and show promising results.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114875973","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}
In this work we analyze the contribution of preprocessing steps for Latin handwriting recognition. A preprocessing pipeline based on geometric heuristics and image statistics is used. This pipeline is applied to French and English handwriting recognition in an HMM based framework. Results show that preprocessing improves recognition performance for the two tasks. The Maximum Likelihood (ML)-trained HMM system reaches a competitive WER of 16.7% and outperforms many sophisticated systems for the French handwriting recognition task. The results for English handwriting are comparable to other ML-trained HMM recognizers. Using MLP preprocessing a WER of 35.3% is achieved.
{"title":"Analysis of Preprocessing Techniques for Latin Handwriting Recognition","authors":"H. Pesch, M. Hamdani, Jens Forster, H. Ney","doi":"10.1109/ICFHR.2012.179","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.179","url":null,"abstract":"In this work we analyze the contribution of preprocessing steps for Latin handwriting recognition. A preprocessing pipeline based on geometric heuristics and image statistics is used. This pipeline is applied to French and English handwriting recognition in an HMM based framework. Results show that preprocessing improves recognition performance for the two tasks. The Maximum Likelihood (ML)-trained HMM system reaches a competitive WER of 16.7% and outperforms many sophisticated systems for the French handwriting recognition task. The results for English handwriting are comparable to other ML-trained HMM recognizers. Using MLP preprocessing a WER of 35.3% is achieved.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127596468","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}
Textual document image denoising is the main issue of this work. Therefore, we introduce a comparative study between two state-of-the-art denoising frameworks : local and non-local smoothing filters. The choice of both of these frameworks is directly related to their ability to deal with local data corruption and to process oriented patterns, a major characteristic of textual documents. Local smoothing filters incorporate anisotropic diffusion approaches where as non-local filters introduce non-local means. Experiments conducted on synthetic and real degraded document images illustrate the behaviour of the studied frameworks on the visual quality and even on the optical recognition accuracy rates.
{"title":"Denoising Textual Images Using Local/Non-local Smoothing Filters: A Comparative Study","authors":"Fadoua Drira, Frank Lebourgeois","doi":"10.1109/ICFHR.2012.198","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.198","url":null,"abstract":"Textual document image denoising is the main issue of this work. Therefore, we introduce a comparative study between two state-of-the-art denoising frameworks : local and non-local smoothing filters. The choice of both of these frameworks is directly related to their ability to deal with local data corruption and to process oriented patterns, a major characteristic of textual documents. Local smoothing filters incorporate anisotropic diffusion approaches where as non-local filters introduce non-local means. Experiments conducted on synthetic and real degraded document images illustrate the behaviour of the studied frameworks on the visual quality and even on the optical recognition accuracy rates.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"9 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121951846","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}
Writer identification is important for forensic analysis, helping experts to deliberate on the authenticity of documents. The general objective of the ICFHR 2012 Writer Identification Contest is to record recent advances in the field of writer identification using established evaluation performance measures. Challenge 1 of the contest deals specifically with Latin scripts. The benchmarking dataset of challenge 1 of the contest was created with the help of 100 writers that were asked to copy four parts of text in two languages (English and Greek). This paper describes the contest details for this challenge including the evaluation measures used as well as the performance of the seven submitted methods along with a short description of each method.
{"title":"ICFHR 2012 Competition on Writer Identification Challenge 1: Latin/Greek Documents","authors":"G. Louloudis, B. Gatos, N. Stamatopoulos","doi":"10.1109/ICFHR.2012.219","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.219","url":null,"abstract":"Writer identification is important for forensic analysis, helping experts to deliberate on the authenticity of documents. The general objective of the ICFHR 2012 Writer Identification Contest is to record recent advances in the field of writer identification using established evaluation performance measures. Challenge 1 of the contest deals specifically with Latin scripts. The benchmarking dataset of challenge 1 of the contest was created with the help of 100 writers that were asked to copy four parts of text in two languages (English and Greek). This paper describes the contest details for this challenge including the evaluation measures used as well as the performance of the seven submitted methods along with a short description of each method.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123831216","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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