Pub Date : 2001-09-10DOI: 10.1109/ICDAR.2001.953828
J. Guo, Matthew Y. Ma
In this paper, we address the problem of separating handwritten annotations from machine-printed text within a document. We present an algorithm that is based on the theory of hidden Markov models (HMMs) to distinguish between machine-printed and handwritten materials. No OCR results are required prior to or during the process, and the classification is performed at the word level. Handwritten annotations are not limited to marginal areas, as the approach can deal with document images having handwritten annotations overlaid on machine-printed text and it has been shown to be promising in our experiments. Experimental results show that the proposed method can achieve 72.19% recall for fully extracted handwritten words and 90.37% for partially extracted words. The precision of extracting handwritten words has reached 92.86%.
{"title":"Separating handwritten material from machine printed text using hidden Markov models","authors":"J. Guo, Matthew Y. Ma","doi":"10.1109/ICDAR.2001.953828","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953828","url":null,"abstract":"In this paper, we address the problem of separating handwritten annotations from machine-printed text within a document. We present an algorithm that is based on the theory of hidden Markov models (HMMs) to distinguish between machine-printed and handwritten materials. No OCR results are required prior to or during the process, and the classification is performed at the word level. Handwritten annotations are not limited to marginal areas, as the approach can deal with document images having handwritten annotations overlaid on machine-printed text and it has been shown to be promising in our experiments. Experimental results show that the proposed method can achieve 72.19% recall for fully extracted handwritten words and 90.37% for partially extracted words. The precision of extracting handwritten words has reached 92.86%.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115375653","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 : 2001-09-10DOI: 10.1109/ICDAR.2001.953840
V. Vuori, Jorma T. Laaksonen, E. Oja, J. Kangas
This work describes a prototype-based online handwritten character recognition system and a two-phase recognition scheme aimed to speed up the recognition. In the first phase, the prototype set is pruned and ordered on the basis of preclassification performed with heavily down-sampled characters and prototypes. In the second phase, the final classification is performed without down-sampling by using the reduced set of prototypes. Two down-sampling methods, a linear and nonlinear one, have been analyzed to see their properties regarding the recognition time and accuracy.
{"title":"Speeding up on-line recognition of handwritten characters by pruning the prototype set","authors":"V. Vuori, Jorma T. Laaksonen, E. Oja, J. Kangas","doi":"10.1109/ICDAR.2001.953840","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953840","url":null,"abstract":"This work describes a prototype-based online handwritten character recognition system and a two-phase recognition scheme aimed to speed up the recognition. In the first phase, the prototype set is pruned and ordered on the basis of preclassification performed with heavily down-sampled characters and prototypes. In the second phase, the final classification is performed without down-sampling by using the reduced set of prototypes. Two down-sampling methods, a linear and nonlinear one, have been analyzed to see their properties regarding the recognition time and accuracy.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114354500","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 : 2001-09-10DOI: 10.1109/ICDAR.2001.953833
Gemma Sánchez, J. Lladós
This paper describes a graph grammar to modelize textured symbols in a graphics recognition framework. A textured symbol means a symbol consisting of repetitive structured patterns. We propose a method to infer a graph grammar from a structured texture detected in a document, and the subsequent parser to decide whether a symbol is accepted by the grammar. The grammar is based on a region adjacency graph representation of the vectorized document and the productions are based on the neighboring relations of the patterns forming the textured symbol. The syntactic framework is applied on an architectural plan understanding application.
{"title":"A graph grammar to recognize textured symbols","authors":"Gemma Sánchez, J. Lladós","doi":"10.1109/ICDAR.2001.953833","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953833","url":null,"abstract":"This paper describes a graph grammar to modelize textured symbols in a graphics recognition framework. A textured symbol means a symbol consisting of repetitive structured patterns. We propose a method to infer a graph grammar from a structured texture detected in a document, and the subsequent parser to decide whether a symbol is accepted by the grammar. The grammar is based on a region adjacency graph representation of the vectorized document and the productions are based on the neighboring relations of the patterns forming the textured symbol. The syntactic framework is applied on an architectural plan understanding application.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114732987","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 : 2001-09-10DOI: 10.1109/ICDAR.2001.953911
A. Brakensiek, J. Rottland, F. Wallhoff, G. Rigoll
A scheme for handwriting adaptation for post offices is described to improve recognition performance of German addresses. The recognition system is based on a tied-mixture hidden Markov model, whose parameters are updated using the expectation maximization technique, the maximum likelihood linear regression algorithm and a new discriminative adaptation technique, the scaled likelihood linear regression. Contrary to the usual approach of adapting a writer-independent system to a specific writer we propose to adapt the system to the writer-independent data of a specific post office. The resulting system for each post office yields up to 16% lower word recognition errors.
{"title":"Adaptation of an address reading system to local mail streams","authors":"A. Brakensiek, J. Rottland, F. Wallhoff, G. Rigoll","doi":"10.1109/ICDAR.2001.953911","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953911","url":null,"abstract":"A scheme for handwriting adaptation for post offices is described to improve recognition performance of German addresses. The recognition system is based on a tied-mixture hidden Markov model, whose parameters are updated using the expectation maximization technique, the maximum likelihood linear regression algorithm and a new discriminative adaptation technique, the scaled likelihood linear regression. Contrary to the usual approach of adapting a writer-independent system to a specific writer we propose to adapt the system to the writer-independent data of a specific post office. The resulting system for each post office yields up to 16% lower word recognition errors.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114822172","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 : 2001-09-10DOI: 10.1109/ICDAR.2001.953793
Gregory N. Hullender
Many problems in recognition involve making linear combinations of results from various experts. Computing the coefficients can be expensive because a single test run can take many minutes, hours, or even days, and many values need to be evaluated to find an optimum. This paper describes an algorithm that can make a good approximation to the optimum value for a parameter in a single pass over the tuning data and also outlines methods for tuning several parameters at once.
{"title":"An efficient method for tuning handwriting parameters","authors":"Gregory N. Hullender","doi":"10.1109/ICDAR.2001.953793","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953793","url":null,"abstract":"Many problems in recognition involve making linear combinations of results from various experts. Computing the coefficients can be expensive because a single test run can take many minutes, hours, or even days, and many values need to be evaluated to find an optimum. This paper describes an algorithm that can make a good approximation to the optimum value for a parameter in a single pass over the tuning data and also outlines methods for tuning several parameters at once.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117092724","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 : 2001-09-10DOI: 10.1109/ICDAR.2001.953922
H. Hase, M. Yoneda, Toshiyuki Shinokawa, C. Suen
A realignment algorithm for irregular character strings on color documents is proposed. Color documents often contain poorly aligned texts such as inclined or curved texts sometimes with distortion. In order to recognize them, we classify these texts into five types. After determining the type, we realign all the characters in a text horizontally, then test them with an ordinary character recognition method. Lastly, we show some experimental results for texts extracted from real color documents and discuss some causes of misrecognition.
{"title":"Alignment of free layout color texts for character recognition","authors":"H. Hase, M. Yoneda, Toshiyuki Shinokawa, C. Suen","doi":"10.1109/ICDAR.2001.953922","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953922","url":null,"abstract":"A realignment algorithm for irregular character strings on color documents is proposed. Color documents often contain poorly aligned texts such as inclined or curved texts sometimes with distortion. In order to recognize them, we classify these texts into five types. After determining the type, we realign all the characters in a text horizontally, then test them with an ordinary character recognition method. Lastly, we show some experimental results for texts extracted from real color documents and discuss some causes of misrecognition.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123298049","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 : 2001-09-10DOI: 10.1109/ICDAR.2001.953903
Don Sylwester, S. Seth
A single-parameter text-line extraction algorithm is described along with an efficient technique for estimating the optimal value for the parameter for individual images without need for ground truth. The algorithm is based on three simple tree operations, cut, glue and flip. An XY-tree representing the segmentation is incrementally transformed to reflect a change in the parameter while intrinsic measures of the cost of the transformation are used to detect when specific tree operations would cause an error if they were performed, allowing these errors to be avoided. The algorithm correctly identified 98.8% of the area of the ground truth bounding boxes and committed no column bridging errors on a set of 97 test images selected from a variety of technical journals.
{"title":"Adaptive segmentation of document images","authors":"Don Sylwester, S. Seth","doi":"10.1109/ICDAR.2001.953903","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953903","url":null,"abstract":"A single-parameter text-line extraction algorithm is described along with an efficient technique for estimating the optimal value for the parameter for individual images without need for ground truth. The algorithm is based on three simple tree operations, cut, glue and flip. An XY-tree representing the segmentation is incrementally transformed to reflect a change in the parameter while intrinsic measures of the cost of the transformation are used to detect when specific tree operations would cause an error if they were performed, allowing these errors to be avoided. The algorithm correctly identified 98.8% of the area of the ground truth bounding boxes and committed no column bridging errors on a set of 97 test images selected from a variety of technical journals.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124828027","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 : 2001-09-10DOI: 10.1109/ICDAR.2001.953914
Jun Zhou, C. Suen, Ke Liu
The proposed feedback-based approach is implemented in two steps. In the first step, segmentation is done according to the structural features between the connected components in the legal amounts. In the second step, a feedback process is introduced to re-segment the parts that could not be identified in the first step. Then a multiple neural network classifier is used to verify the re-segmentation result. The confidence value produced by the classifier is used to determine the best segmentation points. This approach is tested on a CENPARMI database and the result indicates that the correct segmentation rate increased by 13.4% from the previous approach.
{"title":"A feedback-based approach for segmenting handwritten legal amounts on bank cheques","authors":"Jun Zhou, C. Suen, Ke Liu","doi":"10.1109/ICDAR.2001.953914","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953914","url":null,"abstract":"The proposed feedback-based approach is implemented in two steps. In the first step, segmentation is done according to the structural features between the connected components in the legal amounts. In the second step, a feedback process is introduced to re-segment the parts that could not be identified in the first step. Then a multiple neural network classifier is used to verify the re-segmentation result. The confidence value produced by the classifier is used to determine the best segmentation points. This approach is tested on a CENPARMI database and the result indicates that the correct segmentation rate increased by 13.4% from the previous approach.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"52 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113974147","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 : 2001-09-10DOI: 10.1109/ICDAR.2001.953941
H. Kang, Seong-Whan Lee
Only a few studies have been conducted on how to select multiple classifiers from the pool of available classifiers for showing good performance. The selection problem of classifiers on how to select or how many to select still remains an important issue. In this paper, provided that the number of selected classifiers is constrained in advance, a number of selection criteria are proposed and applied to the construction of multiple classifiers. All the sets of classifiers are examined by the selection criteria under the constraint of the number of selected classifiers, and then some of those sets are selected as the candidates of multiple classifier systems. The multiple classifier system candidates were evaluated by the experiments recognizing UCI handwritten numerals.
{"title":"Experimental results on the construction of multiple classifiers recognizing handwritten numerals","authors":"H. Kang, Seong-Whan Lee","doi":"10.1109/ICDAR.2001.953941","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953941","url":null,"abstract":"Only a few studies have been conducted on how to select multiple classifiers from the pool of available classifiers for showing good performance. The selection problem of classifiers on how to select or how many to select still remains an important issue. In this paper, provided that the number of selected classifiers is constrained in advance, a number of selection criteria are proposed and applied to the construction of multiple classifiers. All the sets of classifiers are examined by the selection criteria under the constraint of the number of selected classifiers, and then some of those sets are selected as the candidates of multiple classifier systems. The multiple classifier system candidates were evaluated by the experiments recognizing UCI handwritten numerals.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122850062","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 : 2001-09-10DOI: 10.1109/ICDAR.2001.953934
Dariusz Z. Lejtman, Susan E. George
This paper investigates dynamic handwritten signature verification (HSV) using the wavelet transform with verification by the backpropagation neural network (NN). It is yet another avenue in the approach to HSV that is found to produce excellent results when compared with other methods of dynamic, or on-line, HSV. Using a database of dynamic signatures collected from 41 Chinese writers and 7 from Latin script we extract features (including pen pressure, x and y velocity, angle of pen movement and angular velocity) from the signature and apply the Daubechies-6 wavelet transform using coefficients as input to a NN which learns to verify signatures with a False Rejection Rate (FRR) of 0.0% and False Acceptance Rate (FAR) less of than 0.1.
{"title":"On-line handwritten signature verification using wavelets and back-propagation neural networks","authors":"Dariusz Z. Lejtman, Susan E. George","doi":"10.1109/ICDAR.2001.953934","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953934","url":null,"abstract":"This paper investigates dynamic handwritten signature verification (HSV) using the wavelet transform with verification by the backpropagation neural network (NN). It is yet another avenue in the approach to HSV that is found to produce excellent results when compared with other methods of dynamic, or on-line, HSV. Using a database of dynamic signatures collected from 41 Chinese writers and 7 from Latin script we extract features (including pen pressure, x and y velocity, angle of pen movement and angular velocity) from the signature and apply the Daubechies-6 wavelet transform using coefficients as input to a NN which learns to verify signatures with a False Rejection Rate (FRR) of 0.0% and False Acceptance Rate (FAR) less of than 0.1.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124118840","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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