Pub Date : 2001-09-10DOI: 10.1109/ICDAR.2001.953746
K. Kim, Y. Chung, Jinho Kim, C. Suen
This paper presents recognition of unconstrained handwritten numeral strings using a decision value generator. The numeral string recognition system is composed of three modules: pre-segmentation, segmentation and recognition. The pre-segmentation module classifies a numeral string into sub-images, such as isolated digits, touching digits or broken digits, based on the confidence value of decision value generator. The segmentation module splits the touching digits using the reliability value of decision value generator. Both segmentation-based and segmentation free methods are used in classification and segmentation. To evaluate the proposed method, experiments were conducted using the handwritten numeral strings of NIST SD19 and a higher recognition performance than previous works was obtained.
{"title":"Recognition of unconstrained handwritten numeral strings using decision value generator","authors":"K. Kim, Y. Chung, Jinho Kim, C. Suen","doi":"10.1109/ICDAR.2001.953746","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953746","url":null,"abstract":"This paper presents recognition of unconstrained handwritten numeral strings using a decision value generator. The numeral string recognition system is composed of three modules: pre-segmentation, segmentation and recognition. The pre-segmentation module classifies a numeral string into sub-images, such as isolated digits, touching digits or broken digits, based on the confidence value of decision value generator. The segmentation module splits the touching digits using the reliability value of decision value generator. Both segmentation-based and segmentation free methods are used in classification and segmentation. To evaluate the proposed method, experiments were conducted using the handwritten numeral strings of NIST SD19 and a higher recognition performance than previous works was obtained.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"105 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":"133126699","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.2001.953795
Urs-Viktor Marti, H. Bunke
In this paper we present a system for unconstrained handwritten text recognition. The system consists of three components: preprocessing, feature extraction and recognition. In the preprocessing phase, a page of handwritten text is divided into its lines and the writing is normalized by means of skew and slant correction, positioning and scaling. From a normalized text line image, features are extracted using a sliding window technique. From each position of the window nine geometrical features are computed. The core of the system, the recognizes is based on hidden Markov models. For each individual character, a model is provided. The character models are concatenated to words using a vocabulary. Moreover, the word models are concatenated to models that represent full lines of text. Thus the difficult problem of segmenting a line of text into its individual words can be overcome. To enhance the recognition capabilities of the system, a statistical language model is integrated into the hidden Markov model framework. To preselect useful language models and compare them, perplexity is used. Both perplexity as originally proposed and normalized perplexity are considered.
{"title":"On the influence of vocabulary size and language models in unconstrained handwritten text recognition","authors":"Urs-Viktor Marti, H. Bunke","doi":"10.1109/ICDAR.2001.953795","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953795","url":null,"abstract":"In this paper we present a system for unconstrained handwritten text recognition. The system consists of three components: preprocessing, feature extraction and recognition. In the preprocessing phase, a page of handwritten text is divided into its lines and the writing is normalized by means of skew and slant correction, positioning and scaling. From a normalized text line image, features are extracted using a sliding window technique. From each position of the window nine geometrical features are computed. The core of the system, the recognizes is based on hidden Markov models. For each individual character, a model is provided. The character models are concatenated to words using a vocabulary. Moreover, the word models are concatenated to models that represent full lines of text. Thus the difficult problem of segmenting a line of text into its individual words can be overcome. To enhance the recognition capabilities of the system, a statistical language model is integrated into the hidden Markov model framework. To preselect useful language models and compare them, perplexity is used. Both perplexity as originally proposed and normalized perplexity are considered.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"38 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":"114013725","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.2001.953911
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.2001.953828
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.2001.953922
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.2001.953867
Xue Gao, Lianwen Jin, Junxun Yin, Jiancheng Huang
A directional feature extraction approach based on stroke directional decomposition of a Chinese character is proposed. Without extracting the skeleton or contour of the character, the four directional sub-patterns, namely, horizontal (-), vertical (|), left up diagonal (/) and right up diagonal () sub-patterns could be obtained directly from analyzing the stroke directional characteristics of the character. Five kinds of line-density based elastic meshing methods are presented to extract cellular directional features. Experimentation on a total of 18800 handwritten samples from 940 categories produces a recognition rate of 92.71%, showing the effectiveness of the proposed approach.
{"title":"A new stroke-based directional feature extraction approach for handwritten Chinese character recognition","authors":"Xue Gao, Lianwen Jin, Junxun Yin, Jiancheng Huang","doi":"10.1109/ICDAR.2001.953867","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953867","url":null,"abstract":"A directional feature extraction approach based on stroke directional decomposition of a Chinese character is proposed. Without extracting the skeleton or contour of the character, the four directional sub-patterns, namely, horizontal (-), vertical (|), left up diagonal (/) and right up diagonal () sub-patterns could be obtained directly from analyzing the stroke directional characteristics of the character. Five kinds of line-density based elastic meshing methods are presented to extract cellular directional features. Experimentation on a total of 18800 handwritten samples from 940 categories produces a recognition rate of 92.71%, showing the effectiveness of the proposed approach.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"343 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":"124308471","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.2001.953934
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}
Pub Date : 2001-09-10DOI: 10.1109/ICDAR.2001.953815
Nicolas Roussel, O. Hitz, R. Ingold
The paper presents ongoing work on the design of a Web-based framework for cooperative document understanding. The authors begin by exposing their motivations for designing a new document understanding environment. They then describe the different levels of cooperation they intend to support and how Web technologies can help in this respect. Finally, the authors present Edelweiss, the framework we currently being developing based on this approach.
{"title":"Web-based cooperative document understanding","authors":"Nicolas Roussel, O. Hitz, R. Ingold","doi":"10.1109/ICDAR.2001.953815","DOIUrl":"https://doi.org/10.1109/ICDAR.2001.953815","url":null,"abstract":"The paper presents ongoing work on the design of a Web-based framework for cooperative document understanding. The authors begin by exposing their motivations for designing a new document understanding environment. They then describe the different levels of cooperation they intend to support and how Web technologies can help in this respect. Finally, the authors present Edelweiss, the framework we currently being developing based on this approach.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"39 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":"129830057","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：