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B S Anami - One of the best experts on this subject based on the ideXlab platform.
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A soft computing model for mapping incomplete/approximate Postal Addresses to mail delivery points
Applied Soft Computing, 2009Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:Mapping of Postal Address to a mail delivery point is a very important task that affects the efficiency of Postal service. This task is very complex in the countries such as India, where Postal Addresses are not structured. Further most of the times the destination Addresses in such countries are incomplete, approximate and erroneous which adds to the complexity of mapping Postal Address to delivery point. Automation of this aspect of the Postal service is a challenge. This paper presents a soft computing model to map the Postal Address to mail delivery point. Firstly machine readable Postal Address is processed to identify the Address components using a novel fuzzy symbolic similarity analysis, and further these labeled components are organized as a symbolic Postal Address object. This Postal Address object is further processed using the newly devised fuzzy symbolic methodology for mapping the Address to mail delivery point. Symbolic knowledge bases for Postal Address component labeling and mail delivery point mapping are devised. Fuzzy symbolic similarity measures are formulated once for Address component labeling and the second time for mapping the entire Address to a mail delivery point. In sequel to similarity computations, which are viewed as fuzzy membership values, an expert system comprising of @a-cut de-fuzzification is proposed to evaluate the confidence factors, while inferencing the validity of Address component labels and mail delivery points. The system is tested exhaustively and an efficiency of 94% is obtained in Address component identification and about 86% in mail delivery point mapping, while working on an Indian Postal data base of about 500 Addresses.
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a fuzzy symbolic inference system for Postal Address component extraction and labelling
Fuzzy Systems and Knowledge Discovery, 2006Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:It is important to properly segregate the different components present in the destination Postal Address under different labels namely Addressee name, house number, street number, extension/ area name, destination town name and the like for automatic Address reading. This task is not as easy as it would appear particularly for unstructured Postal Addresses such as that are found in India. This paper presents a fuzzy symbolic inference system for Postal mail Address component extraction and labelling. The work uses a symbolic representation for Postal Addresses and a symbolic knowledge base for Postal Address component labelling. A symbolic similarity measure treated as a fuzzy membership function is devised and is used for finding the distance of the extracted component to a probable label. An alpha cut based de-fuzzification technique is employed for labelling and evaluation of confidence in the decision. The methodology is tested on 500 Postal Addresses and an efficiency of 94% is obtained for Address component labeling.
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FSKD - A fuzzy symbolic inference system for Postal Address component extraction and labelling
Fuzzy Systems and Knowledge Discovery, 2006Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:It is important to properly segregate the different components present in the destination Postal Address under different labels namely Addressee name, house number, street number, extension/ area name, destination town name and the like for automatic Address reading. This task is not as easy as it would appear particularly for unstructured Postal Addresses such as that are found in India. This paper presents a fuzzy symbolic inference system for Postal mail Address component extraction and labelling. The work uses a symbolic representation for Postal Addresses and a symbolic knowledge base for Postal Address component labelling. A symbolic similarity measure treated as a fuzzy membership function is devised and is used for finding the distance of the extracted component to a probable label. An alpha cut based de-fuzzification technique is employed for labelling and evaluation of confidence in the decision. The methodology is tested on 500 Postal Addresses and an efficiency of 94% is obtained for Address component labeling.
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symbolic data structure for Postal Address representation and Address validation through symbolic knowledge base
Pattern Recognition and Machine Intelligence, 2005Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:The Postal Address data and the domain information for Address validation contain qualitative, numeric, interval and other types of data. The efficient processing of such data required for Postal automation needs a robust data structure that facilitates their storage and access. A symbolic data structure is proposed to represent the Postal Address and the information relevant for validating the Postal Address is stored in a newly devised symbolic knowledge base. The symbolic representation gives a formal structure to the information and hence is more beneficial than other representations such as frames, which do not reflect the structure inherent in the domain knowledge. The process of Postal Address validation checks the different components of the Postal Address for consistency before using it for further processing. In the present work a symbolic knowledge base supported Address validation system is developed and tested for about 500 Addresses. The system efficiency is observed to be 95.6% in validating the Addresses automatically.
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PReMI - Symbolic data structure for Postal Address representation and Address validation through symbolic knowledge base
Lecture Notes in Computer Science, 2005Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:The Postal Address data and the domain information for Address validation contain qualitative, numeric, interval and other types of data. The efficient processing of such data required for Postal automation needs a robust data structure that facilitates their storage and access. A symbolic data structure is proposed to represent the Postal Address and the information relevant for validating the Postal Address is stored in a newly devised symbolic knowledge base. The symbolic representation gives a formal structure to the information and hence is more beneficial than other representations such as frames, which do not reflect the structure inherent in the domain knowledge. The process of Postal Address validation checks the different components of the Postal Address for consistency before using it for further processing. In the present work a symbolic knowledge base supported Address validation system is developed and tested for about 500 Addresses. The system efficiency is observed to be 95.6% in validating the Addresses automatically.
P Nagabhushan - One of the best experts on this subject based on the ideXlab platform.
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A soft computing model for mapping incomplete/approximate Postal Addresses to mail delivery points
Applied Soft Computing, 2009Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:Mapping of Postal Address to a mail delivery point is a very important task that affects the efficiency of Postal service. This task is very complex in the countries such as India, where Postal Addresses are not structured. Further most of the times the destination Addresses in such countries are incomplete, approximate and erroneous which adds to the complexity of mapping Postal Address to delivery point. Automation of this aspect of the Postal service is a challenge. This paper presents a soft computing model to map the Postal Address to mail delivery point. Firstly machine readable Postal Address is processed to identify the Address components using a novel fuzzy symbolic similarity analysis, and further these labeled components are organized as a symbolic Postal Address object. This Postal Address object is further processed using the newly devised fuzzy symbolic methodology for mapping the Address to mail delivery point. Symbolic knowledge bases for Postal Address component labeling and mail delivery point mapping are devised. Fuzzy symbolic similarity measures are formulated once for Address component labeling and the second time for mapping the entire Address to a mail delivery point. In sequel to similarity computations, which are viewed as fuzzy membership values, an expert system comprising of @a-cut de-fuzzification is proposed to evaluate the confidence factors, while inferencing the validity of Address component labels and mail delivery points. The system is tested exhaustively and an efficiency of 94% is obtained in Address component identification and about 86% in mail delivery point mapping, while working on an Indian Postal data base of about 500 Addresses.
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a fuzzy symbolic inference system for Postal Address component extraction and labelling
Fuzzy Systems and Knowledge Discovery, 2006Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:It is important to properly segregate the different components present in the destination Postal Address under different labels namely Addressee name, house number, street number, extension/ area name, destination town name and the like for automatic Address reading. This task is not as easy as it would appear particularly for unstructured Postal Addresses such as that are found in India. This paper presents a fuzzy symbolic inference system for Postal mail Address component extraction and labelling. The work uses a symbolic representation for Postal Addresses and a symbolic knowledge base for Postal Address component labelling. A symbolic similarity measure treated as a fuzzy membership function is devised and is used for finding the distance of the extracted component to a probable label. An alpha cut based de-fuzzification technique is employed for labelling and evaluation of confidence in the decision. The methodology is tested on 500 Postal Addresses and an efficiency of 94% is obtained for Address component labeling.
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FSKD - A fuzzy symbolic inference system for Postal Address component extraction and labelling
Fuzzy Systems and Knowledge Discovery, 2006Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:It is important to properly segregate the different components present in the destination Postal Address under different labels namely Addressee name, house number, street number, extension/ area name, destination town name and the like for automatic Address reading. This task is not as easy as it would appear particularly for unstructured Postal Addresses such as that are found in India. This paper presents a fuzzy symbolic inference system for Postal mail Address component extraction and labelling. The work uses a symbolic representation for Postal Addresses and a symbolic knowledge base for Postal Address component labelling. A symbolic similarity measure treated as a fuzzy membership function is devised and is used for finding the distance of the extracted component to a probable label. An alpha cut based de-fuzzification technique is employed for labelling and evaluation of confidence in the decision. The methodology is tested on 500 Postal Addresses and an efficiency of 94% is obtained for Address component labeling.
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symbolic data structure for Postal Address representation and Address validation through symbolic knowledge base
Pattern Recognition and Machine Intelligence, 2005Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:The Postal Address data and the domain information for Address validation contain qualitative, numeric, interval and other types of data. The efficient processing of such data required for Postal automation needs a robust data structure that facilitates their storage and access. A symbolic data structure is proposed to represent the Postal Address and the information relevant for validating the Postal Address is stored in a newly devised symbolic knowledge base. The symbolic representation gives a formal structure to the information and hence is more beneficial than other representations such as frames, which do not reflect the structure inherent in the domain knowledge. The process of Postal Address validation checks the different components of the Postal Address for consistency before using it for further processing. In the present work a symbolic knowledge base supported Address validation system is developed and tested for about 500 Addresses. The system efficiency is observed to be 95.6% in validating the Addresses automatically.
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PReMI - Symbolic data structure for Postal Address representation and Address validation through symbolic knowledge base
Lecture Notes in Computer Science, 2005Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:The Postal Address data and the domain information for Address validation contain qualitative, numeric, interval and other types of data. The efficient processing of such data required for Postal automation needs a robust data structure that facilitates their storage and access. A symbolic data structure is proposed to represent the Postal Address and the information relevant for validating the Postal Address is stored in a newly devised symbolic knowledge base. The symbolic representation gives a formal structure to the information and hence is more beneficial than other representations such as frames, which do not reflect the structure inherent in the domain knowledge. The process of Postal Address validation checks the different components of the Postal Address for consistency before using it for further processing. In the present work a symbolic knowledge base supported Address validation system is developed and tested for about 500 Addresses. The system efficiency is observed to be 95.6% in validating the Addresses automatically.
S A Angadi - One of the best experts on this subject based on the ideXlab platform.
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A soft computing model for mapping incomplete/approximate Postal Addresses to mail delivery points
Applied Soft Computing, 2009Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:Mapping of Postal Address to a mail delivery point is a very important task that affects the efficiency of Postal service. This task is very complex in the countries such as India, where Postal Addresses are not structured. Further most of the times the destination Addresses in such countries are incomplete, approximate and erroneous which adds to the complexity of mapping Postal Address to delivery point. Automation of this aspect of the Postal service is a challenge. This paper presents a soft computing model to map the Postal Address to mail delivery point. Firstly machine readable Postal Address is processed to identify the Address components using a novel fuzzy symbolic similarity analysis, and further these labeled components are organized as a symbolic Postal Address object. This Postal Address object is further processed using the newly devised fuzzy symbolic methodology for mapping the Address to mail delivery point. Symbolic knowledge bases for Postal Address component labeling and mail delivery point mapping are devised. Fuzzy symbolic similarity measures are formulated once for Address component labeling and the second time for mapping the entire Address to a mail delivery point. In sequel to similarity computations, which are viewed as fuzzy membership values, an expert system comprising of @a-cut de-fuzzification is proposed to evaluate the confidence factors, while inferencing the validity of Address component labels and mail delivery points. The system is tested exhaustively and an efficiency of 94% is obtained in Address component identification and about 86% in mail delivery point mapping, while working on an Indian Postal data base of about 500 Addresses.
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a fuzzy symbolic inference system for Postal Address component extraction and labelling
Fuzzy Systems and Knowledge Discovery, 2006Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:It is important to properly segregate the different components present in the destination Postal Address under different labels namely Addressee name, house number, street number, extension/ area name, destination town name and the like for automatic Address reading. This task is not as easy as it would appear particularly for unstructured Postal Addresses such as that are found in India. This paper presents a fuzzy symbolic inference system for Postal mail Address component extraction and labelling. The work uses a symbolic representation for Postal Addresses and a symbolic knowledge base for Postal Address component labelling. A symbolic similarity measure treated as a fuzzy membership function is devised and is used for finding the distance of the extracted component to a probable label. An alpha cut based de-fuzzification technique is employed for labelling and evaluation of confidence in the decision. The methodology is tested on 500 Postal Addresses and an efficiency of 94% is obtained for Address component labeling.
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FSKD - A fuzzy symbolic inference system for Postal Address component extraction and labelling
Fuzzy Systems and Knowledge Discovery, 2006Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:It is important to properly segregate the different components present in the destination Postal Address under different labels namely Addressee name, house number, street number, extension/ area name, destination town name and the like for automatic Address reading. This task is not as easy as it would appear particularly for unstructured Postal Addresses such as that are found in India. This paper presents a fuzzy symbolic inference system for Postal mail Address component extraction and labelling. The work uses a symbolic representation for Postal Addresses and a symbolic knowledge base for Postal Address component labelling. A symbolic similarity measure treated as a fuzzy membership function is devised and is used for finding the distance of the extracted component to a probable label. An alpha cut based de-fuzzification technique is employed for labelling and evaluation of confidence in the decision. The methodology is tested on 500 Postal Addresses and an efficiency of 94% is obtained for Address component labeling.
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symbolic data structure for Postal Address representation and Address validation through symbolic knowledge base
Pattern Recognition and Machine Intelligence, 2005Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:The Postal Address data and the domain information for Address validation contain qualitative, numeric, interval and other types of data. The efficient processing of such data required for Postal automation needs a robust data structure that facilitates their storage and access. A symbolic data structure is proposed to represent the Postal Address and the information relevant for validating the Postal Address is stored in a newly devised symbolic knowledge base. The symbolic representation gives a formal structure to the information and hence is more beneficial than other representations such as frames, which do not reflect the structure inherent in the domain knowledge. The process of Postal Address validation checks the different components of the Postal Address for consistency before using it for further processing. In the present work a symbolic knowledge base supported Address validation system is developed and tested for about 500 Addresses. The system efficiency is observed to be 95.6% in validating the Addresses automatically.
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PReMI - Symbolic data structure for Postal Address representation and Address validation through symbolic knowledge base
Lecture Notes in Computer Science, 2005Co-Authors: P Nagabhushan, S A Angadi, B S AnamiAbstract:The Postal Address data and the domain information for Address validation contain qualitative, numeric, interval and other types of data. The efficient processing of such data required for Postal automation needs a robust data structure that facilitates their storage and access. A symbolic data structure is proposed to represent the Postal Address and the information relevant for validating the Postal Address is stored in a newly devised symbolic knowledge base. The symbolic representation gives a formal structure to the information and hence is more beneficial than other representations such as frames, which do not reflect the structure inherent in the domain knowledge. The process of Postal Address validation checks the different components of the Postal Address for consistency before using it for further processing. In the present work a symbolic knowledge base supported Address validation system is developed and tested for about 500 Addresses. The system efficiency is observed to be 95.6% in validating the Addresses automatically.
Faisal Shafait - One of the best experts on this subject based on the ideXlab platform.
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deepparse a trainable Postal Address parser
Digital Image Computing: Techniques and Applications, 2018Co-Authors: Nosheen Abid, Adnan Ul Hasan, Faisal ShafaitAbstract:Postal applications are among the first beneficiaries of the advancements in document image processing techniques due to their economic significance. To automate the process of Postal services, it is necessary to integrate contributions from a wide range of image processing domains, from image acquisition and preprocessing to interpretation through symbol, character and word recognition. Lately, machine learning approaches are deployed for Postal Address processing. Parsing problem has been explored using different techniques, like regular expressions, Conditional Random Fields (CRFs), Hidden Markov Models (HMMs), Decision Trees and Support Vector Machines (SVMs). These traditional techniques are designed on the assumption that the data is free from OCR errors which decreases the adaptability of the architecture in the real-world scenarios. Furthermore, their performance is affected in the presence of non-standardized Addresses resulting in intermixing of similar classes. In this paper, we present the first trainable neural network based robust architecture DeepParse for Postal Address parsing that tackles these issues and can be applied to any Named Entity Recognition (NER) problem. The architecture takes the input at different granularity levels: characters, trigram characters and words to extract and learn the features and classify the Addresses. The model was trained on a synthetically generated dataset and tested on the real-world Addresses. DeepParse has also been tested on the NER dataset i.e. CoNLL2003 and gave the result of 90.44% which is on par with the state-of-art technique.
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DICTA - DeepParse: A Trainable Postal Address Parser
2018 Digital Image Computing: Techniques and Applications (DICTA), 2018Co-Authors: Nosheen Abid, Adnan Ul Hasan, Faisal ShafaitAbstract:Postal applications are among the first beneficiaries of the advancements in document image processing techniques due to their economic significance. To automate the process of Postal services, it is necessary to integrate contributions from a wide range of image processing domains, from image acquisition and preprocessing to interpretation through symbol, character and word recognition. Lately, machine learning approaches are deployed for Postal Address processing. Parsing problem has been explored using different techniques, like regular expressions, Conditional Random Fields (CRFs), Hidden Markov Models (HMMs), Decision Trees and Support Vector Machines (SVMs). These traditional techniques are designed on the assumption that the data is free from OCR errors which decreases the adaptability of the architecture in the real-world scenarios. Furthermore, their performance is affected in the presence of non-standardized Addresses resulting in intermixing of similar classes. In this paper, we present the first trainable neural network based robust architecture DeepParse for Postal Address parsing that tackles these issues and can be applied to any Named Entity Recognition (NER) problem. The architecture takes the input at different granularity levels: characters, trigram characters and words to extract and learn the features and classify the Addresses. The model was trained on a synthetically generated dataset and tested on the real-world Addresses. DeepParse has also been tested on the NER dataset i.e. CoNLL2003 and gave the result of 90.44% which is on par with the state-of-art technique.
Udo Miletzki - One of the best experts on this subject based on the ideXlab platform.
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genesis of Postal Address reading current state and future prospects thirty years of pattern recognition on duty of Postal services
Knowledge Discovery and Data Mining, 2008Co-Authors: Udo MiletzkiAbstract:Abstract Intro: An overview is given of the largest industrial OCR application world wide: Postal Address Reading, how it came into being, how it evolved rapidly to its current state-of-the-art and what are its future prospects. Some prominent historical-, system-, methodological-, cultural- and social aspects are illuminated.
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KDD - Genesis of Postal Address reading, current state and future prospects: thirty years of pattern recognition on duty of Postal services
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08, 2008Co-Authors: Udo MiletzkiAbstract:Abstract Intro: An overview is given of the largest industrial OCR application world wide: Postal Address Reading, how it came into being, how it evolved rapidly to its current state-of-the-art and what are its future prospects. Some prominent historical-, system-, methodological-, cultural- and social aspects are illuminated.
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how Postal Address readers are made adaptive
Lecture Notes in Computer Science, 2004Co-Authors: Hartmut Schafer, Thomas Bayer, Klaus Kreuzer, Udo Miletzki, Marcpeter Schambach, Matthias SchulteaustumAbstract:In the following chapter we describe how a Postal Address reader is made adaptive. A Postal Address reader is a huge application, so we concentrate on technologies used to adapt it to a few important tasks. In particular, we describe adaptation strategies for the detectors and classifiers of regions of interest (ROI), for the classifiers for single character recognition, for a hidden Markov recogniser for hand written words and for the Address dictionary of the reader. The described techniques have been deployed in all Postal Address reading applications, including parcel, flat, letter and in-house mail sorting.
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Reading and Learning - How Postal Address Readers Are Made Adaptive
Reading and Learning, 2004Co-Authors: Hartmut Schafer, Thomas Bayer, Klaus Kreuzer, Udo Miletzki, Marcpeter Schambach, Matthias Schulte-austumAbstract:In the following chapter we describe how a Postal Address reader is made adaptive. A Postal Address reader is a huge application, so we concentrate on technologies used to adapt it to a few important tasks. In particular, we describe adaptation strategies for the detectors and classifiers of regions of interest (ROI), for the classifiers for single character recognition, for a hidden Markov recogniser for hand written words and for the Address dictionary of the reader. The described techniques have been deployed in all Postal Address reading applications, including parcel, flat, letter and in-house mail sorting.
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continuous learning systems Postal Address readers with built in learning capability
International Conference on Document Analysis and Recognition, 1999Co-Authors: Udo Miletzki, Thomas Bayer, Hartmut SchaferAbstract:While current Postal Address readers have been adapted and fine tuned once and off-line in a central development lab, the concept of next generation readers presented here will contain a built-in learning capability which can be referred to as continuous learning from letters. This will be performed on different levels of reading and comprehension, enabling the system to adapt itself to slow changes of mail mix and writing conventions. Preconditions of an adaptive reading system are discussed and an outline of such a system is given, which not only keeps track, with the slow changes of input, but also optimizes itself to sire specific conditions, thus guaranteeing an optimal solution at each site and at any time within life cycle. The system presented here is funded by BMBF (German Federal Ministry of Education and Research) within the projects READ and ADAPTIVE READ.