Pub Date : 1997-08-18DOI: 10.1109/ICDAR.1997.620613
Gregory I. Dzuba, Alexander Filatov, A. Volgunin
The encoding of delivery point code (DPC) for a handwritten address is one of the most complex problems of the US mail delivery automation. This paper describes a real-time system intended to recognize the 5-digit ZIP code part of DPC. To increase the system performance the results of ZIP code recognition are cross-validated with those of city and state name recognition. The main principles of the handwritten word recognizer which provide the core of the system are explained. The system throughput is 40,000 address blocks per hour. Experimental results on live mail pieces are presented. The ZIP code recognition rate is 73% with 1% error rate.
{"title":"Handwritten ZIP code recognition","authors":"Gregory I. Dzuba, Alexander Filatov, A. Volgunin","doi":"10.1109/ICDAR.1997.620613","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.620613","url":null,"abstract":"The encoding of delivery point code (DPC) for a handwritten address is one of the most complex problems of the US mail delivery automation. This paper describes a real-time system intended to recognize the 5-digit ZIP code part of DPC. To increase the system performance the results of ZIP code recognition are cross-validated with those of city and state name recognition. The main principles of the handwritten word recognizer which provide the core of the system are explained. The system throughput is 40,000 address blocks per hour. Experimental results on live mail pieces are presented. The ZIP code recognition rate is 73% with 1% error rate.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121784133","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 : 1997-08-18DOI: 10.1109/ICDAR.1997.619882
Y. Ariki, T. Teranishi
In accumulating and retrieving multimedia information such as images, speech and text, it is necessary to compress and retrieve the information efficiently and accurately. The purpose of this paper is to construct a multimedia database of TV news images based on telop character recognition. The first step is to detect telop frames and to segment the characters by differentiating the telop frames based on the fact that character regions have high brightness and the character edges are clear. The second step is the telop character recognition. It is performed by a subspace method using direction histogram features. The third step is indexing by extracting noun words after morphological analysis of the recognized telop characters. These noun words correspond with key words and are given to TV news articles as their indices. Finally TV news articles are classified into 10 topics such as politics, economics, culture, amusements, sports and so on based on the extracted indices. We employed an index-topic table to classify the articles using indices. The telop character recognition rate was 65.7% and the article classification rate was 67.3%.
{"title":"Indexing and classification of TV news articles based on telop recognition","authors":"Y. Ariki, T. Teranishi","doi":"10.1109/ICDAR.1997.619882","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.619882","url":null,"abstract":"In accumulating and retrieving multimedia information such as images, speech and text, it is necessary to compress and retrieve the information efficiently and accurately. The purpose of this paper is to construct a multimedia database of TV news images based on telop character recognition. The first step is to detect telop frames and to segment the characters by differentiating the telop frames based on the fact that character regions have high brightness and the character edges are clear. The second step is the telop character recognition. It is performed by a subspace method using direction histogram features. The third step is indexing by extracting noun words after morphological analysis of the recognized telop characters. These noun words correspond with key words and are given to TV news articles as their indices. Finally TV news articles are classified into 10 topics such as politics, economics, culture, amusements, sports and so on based on the extracted indices. We employed an index-topic table to classify the articles using indices. The telop character recognition rate was 65.7% and the article classification rate was 67.3%.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122875049","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 : 1997-08-18DOI: 10.1109/ICDAR.1997.620629
A. Benedetti, Z. Kovács-Vajna
An expression in closed form is derived for the recognition error vs. rejection rate of optical character or word recognition systems. This expression allows to define a lower bound for the error rate of any recognition system employing a rejection process based on the definition of a confidence threshold. This relation has also proved to be useful to make a quantitative comparison between two confidence computation methods implemented in a system for reading USA Census '90 hand-written forms. The newly proposed method is based upon a confidence model integrating single-character confidence levels, digram statistics and other information from the dictionary matching phase. At a 50% rejection rate, the field error rate calculated using the new confidence computation algorithm decreased from 47.7% to 44.6%, which represents a considerable improvement, given a theoretical lower bound of 40.8% on the error rate.
{"title":"Confidence computation improvement in an optical field reading system","authors":"A. Benedetti, Z. Kovács-Vajna","doi":"10.1109/ICDAR.1997.620629","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.620629","url":null,"abstract":"An expression in closed form is derived for the recognition error vs. rejection rate of optical character or word recognition systems. This expression allows to define a lower bound for the error rate of any recognition system employing a rejection process based on the definition of a confidence threshold. This relation has also proved to be useful to make a quantitative comparison between two confidence computation methods implemented in a system for reading USA Census '90 hand-written forms. The newly proposed method is based upon a confidence model integrating single-character confidence levels, digram statistics and other information from the dictionary matching phase. At a 50% rejection rate, the field error rate calculated using the new confidence computation algorithm decreased from 47.7% to 44.6%, which represents a considerable improvement, given a theoretical lower bound of 40.8% on the error rate.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122955681","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 : 1997-08-18DOI: 10.1109/ICDAR.1997.620558
S. Naoi, Maki Yabuki
The global interpolation method we propose can extract a handwritten alpha-numeric character pattern even if it overlaps a border. Our method interpolates blank segments in a character after borders are removed by globally evaluating segment label connectivity and connectedness to produce characters with smooth edges. However, the method cannot interpolate missing superpositioning segments, such as an overlapping horizontal line in the number "2". To solve this problem, we propose a global interpolation method II which adds top-down recognition processing to the bottom-up processing of the existing global interpolation method by automatically acquiring knowledge of the relationship between the overlapped condition and recognition reliability. Experimental results which use generated overlapping characters using the ETL database showed that our global interpolation method II has almost the same accuracy as the original ETL database.
{"title":"Global interpolation method II for handwritten numbers overlapping a border by automatic knowledge acquisition of overlapped conditions","authors":"S. Naoi, Maki Yabuki","doi":"10.1109/ICDAR.1997.620558","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.620558","url":null,"abstract":"The global interpolation method we propose can extract a handwritten alpha-numeric character pattern even if it overlaps a border. Our method interpolates blank segments in a character after borders are removed by globally evaluating segment label connectivity and connectedness to produce characters with smooth edges. However, the method cannot interpolate missing superpositioning segments, such as an overlapping horizontal line in the number \"2\". To solve this problem, we propose a global interpolation method II which adds top-down recognition processing to the bottom-up processing of the existing global interpolation method by automatically acquiring knowledge of the relationship between the overlapped condition and recognition reliability. Experimental results which use generated overlapping characters using the ETL database showed that our global interpolation method II has almost the same accuracy as the original ETL database.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133000117","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 : 1997-08-18DOI: 10.1109/ICDAR.1997.619839
A. Amin, Seung-Gwon Kim, C. Sammut
Recognition of Chinese characters has been an area of great interest for many years, and a large number of research papers and reports have already been published in this area. There are several major problems with Chinese character recognition: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate. This paper presents a new technique for the recognition of hand-printed Chinese characters using machine learning C4.5. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The paper also discusses Chinese character recognition using dominant point feature extraction and C4.5. The system was tested with 900 characters (each character has 40 samples) and the rate of recognition obtained was 84%.
{"title":"Hand-printed Chinese character recognition via machine learning","authors":"A. Amin, Seung-Gwon Kim, C. Sammut","doi":"10.1109/ICDAR.1997.619839","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.619839","url":null,"abstract":"Recognition of Chinese characters has been an area of great interest for many years, and a large number of research papers and reports have already been published in this area. There are several major problems with Chinese character recognition: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate. This paper presents a new technique for the recognition of hand-printed Chinese characters using machine learning C4.5. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The paper also discusses Chinese character recognition using dominant point feature extraction and C4.5. The system was tested with 900 characters (each character has 40 samples) and the rate of recognition obtained was 84%.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130141079","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 : 1997-08-18DOI: 10.1109/ICDAR.1997.619836
F. Cesarini, E. Francesconi, M. Gori, S. Marinai, Jianqing Sheng, G. Soda
Much attention has recently been paid to the recognition of graphical objects, such as company logos and trademarks. Recognizing these objects facilitates the recognition of document classes. Some promising results have been achieved by using autoassociator-based artificial neural networks (AANN) in the presence of homogeneously distributed noise. However, the performance drops significantly when dealing with spot-noisy logos, where strips or blobs produce a partial obstruction of the pictures. We propose a new approach for training AANNs especially conceived for dealing with spot noise. The basic idea is to introduce new metrics for assessing the reproduction error in AANNs. The proposed algorithm, referred to as spot-backpropagation (S-BP), is significantly more robust with respect to spot-noise than classical Euclidean norm-based backpropagation (BP). Our experimental results are based on a database of 88 real logos that are artificially corrupted by spot-noise.
{"title":"A neural-based architecture for spot-noisy logo recognition","authors":"F. Cesarini, E. Francesconi, M. Gori, S. Marinai, Jianqing Sheng, G. Soda","doi":"10.1109/ICDAR.1997.619836","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.619836","url":null,"abstract":"Much attention has recently been paid to the recognition of graphical objects, such as company logos and trademarks. Recognizing these objects facilitates the recognition of document classes. Some promising results have been achieved by using autoassociator-based artificial neural networks (AANN) in the presence of homogeneously distributed noise. However, the performance drops significantly when dealing with spot-noisy logos, where strips or blobs produce a partial obstruction of the pictures. We propose a new approach for training AANNs especially conceived for dealing with spot noise. The basic idea is to introduce new metrics for assessing the reproduction error in AANNs. The proposed algorithm, referred to as spot-backpropagation (S-BP), is significantly more robust with respect to spot-noise than classical Euclidean norm-based backpropagation (BP). Our experimental results are based on a database of 88 real logos that are artificially corrupted by spot-noise.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124554860","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 : 1997-08-18DOI: 10.1109/ICDAR.1997.619851
R. Kashi, Jianying Hu, W. Nelson, William Turin
A method for the automatic verification of on-line handwritten signatures using both global and local features as described. The global and local features capture various aspects of signature shape and dynamics of signature production. The authors demonstrate that with the addition to the global features of a local feature based on the signature likelihood obtained from hidden Markov models (HMM) the performance of signature verification improves significantly. The current version of the program, has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%.
{"title":"On-line handwritten signature verification using hidden Markov model features","authors":"R. Kashi, Jianying Hu, W. Nelson, William Turin","doi":"10.1109/ICDAR.1997.619851","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.619851","url":null,"abstract":"A method for the automatic verification of on-line handwritten signatures using both global and local features as described. The global and local features capture various aspects of signature shape and dynamics of signature production. The authors demonstrate that with the addition to the global features of a local feature based on the signature likelihood obtained from hidden Markov models (HMM) the performance of signature verification improves significantly. The current version of the program, has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114762096","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 : 1997-08-18DOI: 10.1109/ICDAR.1997.619842
Youbin Chen, Xiaoqing Ding, Youshou Wu
A new method to extract crossing line features for off-line handwritten Chinese character recognition is proposed in this paper. Firstly, the input pattern is nonlinearly normalized in order to compensate for shape variations. Secondly, the normalized pattern is separated into four subpatterns according to the four kinds of elementary strokes. Thirdly, the four subpatterns are uniformly divided into M/spl times/M cells respectively. In every cell, the crossing lines are counted. Then a 4M/sup 2/-dimensional feature vector is generated. An off-line handwritten Chinese character recognition system is built based on this feature. Our experiments have demonstrated the effectiveness of the method proposed in this paper.
{"title":"Off-line handwritten Chinese character recognition based on crossing line feature","authors":"Youbin Chen, Xiaoqing Ding, Youshou Wu","doi":"10.1109/ICDAR.1997.619842","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.619842","url":null,"abstract":"A new method to extract crossing line features for off-line handwritten Chinese character recognition is proposed in this paper. Firstly, the input pattern is nonlinearly normalized in order to compensate for shape variations. Secondly, the normalized pattern is separated into four subpatterns according to the four kinds of elementary strokes. Thirdly, the four subpatterns are uniformly divided into M/spl times/M cells respectively. In every cell, the crossing lines are counted. Then a 4M/sup 2/-dimensional feature vector is generated. An off-line handwritten Chinese character recognition system is built based on this feature. Our experiments have demonstrated the effectiveness of the method proposed in this paper.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114782584","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 : 1997-08-18DOI: 10.1109/ICDAR.1997.619832
J. Mao, R. Lorie, K. Mohiuddin
We describe a prototype system for reading IATA flight coupons. The system exploits various characteristics of IATA coupons to determine reliably coupon types and field boundaries, and to minimize the amount of manual keying. In particular, we propose a method for extracting and recognizing fixed-pitch characters on noisy images with a complex background. The method does not require a complete drop-out of background, pre-printed text, or lines before recognition, and allows for recovering partially damaged characters (e.g., overlap with form content, handwritten annotations, etc.).
{"title":"A system for automatically reading IATA flight coupons","authors":"J. Mao, R. Lorie, K. Mohiuddin","doi":"10.1109/ICDAR.1997.619832","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.619832","url":null,"abstract":"We describe a prototype system for reading IATA flight coupons. The system exploits various characteristics of IATA coupons to determine reliably coupon types and field boundaries, and to minimize the amount of manual keying. In particular, we propose a method for extracting and recognizing fixed-pitch characters on noisy images with a complex background. The method does not require a complete drop-out of background, pre-printed text, or lines before recognition, and allows for recovering partially damaged characters (e.g., overlap with form content, handwritten annotations, etc.).","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127422069","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 : 1997-08-18DOI: 10.1109/ICDAR.1997.620667
Wang Song, Ma Feng, X. Shaowei
The contradiction between the high recognition accuracy and the low rejection rate in automatic bank check recognition has not been solved successfully. In this paper, a fault-tolerant Chinese bank check recognition system is presented to solve the contradiction between the need for low-error-recognition probability and the need for low-refused-recognition probability. The main idea is to use a dynamic cipher code (which is to be widely applied in China) to lower both of them. This system achieves a high recognition rate and a high reliability simultaneously when automatically processing Chinese bank checks with dynamic cipher codes. A practical scheme of fault-tolerant recognition of bank checks is given in this paper, and experiments show the performance of our fault-tolerant technique.
{"title":"A Chinese bank check recognition system based on the fault tolerant technique","authors":"Wang Song, Ma Feng, X. Shaowei","doi":"10.1109/ICDAR.1997.620667","DOIUrl":"https://doi.org/10.1109/ICDAR.1997.620667","url":null,"abstract":"The contradiction between the high recognition accuracy and the low rejection rate in automatic bank check recognition has not been solved successfully. In this paper, a fault-tolerant Chinese bank check recognition system is presented to solve the contradiction between the need for low-error-recognition probability and the need for low-refused-recognition probability. The main idea is to use a dynamic cipher code (which is to be widely applied in China) to lower both of them. This system achieves a high recognition rate and a high reliability simultaneously when automatically processing Chinese bank checks with dynamic cipher codes. A practical scheme of fault-tolerant recognition of bank checks is given in this paper, and experiments show the performance of our fault-tolerant technique.","PeriodicalId":435320,"journal":{"name":"Proceedings of the Fourth International Conference on Document Analysis and Recognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121011227","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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