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A Simple Context Dependent Suggestion System 22

A Simple Context Dependent Suggestion System

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22nd Text REtrieval Conference, TREC 2013

作者： Roy, Dwaipayan Bandyopadhyay, Ayan Mitra, Mandar Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T.Road Kolkata700108 India

The Contextual Suggestion Problem focuses on search techniques for complex information needs that are highly dependent on context and user interest. In this paper, we present our approach to providing user and context dependent suggestions. The official evaluation scores for our submitted run are reported and compared to the overall median scores (across all 34 runs). © 2013 22nd Text REtrieval Conference, TREC 2013 - Proceedings. All Rights Reserved.

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Multi-script off-line Signature verification: A two stage approach 2

Multi-script off-line Signature verification: A two stage ap...

引用

2nd International Workshop on Automated Forensic Handwriting Analysis, AFHA 2013

作者： Pal, Srikanta Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Griffith University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Signature identification and verification are of great importance in authentication systems. The purpose of this paper is to introduce an experimental contribution in the direction of multi-script off-line signature identification and verification using a novel technique involving off-line English, Hindi (Devnagari) and Bangla (Bengali) signatures. In the first evaluation stage of the proposed signature verification technique, the performance of a multi-script off-line signature verification system, considering a joint dataset of English, Hindi and Bangla signatures, was investigated. In the second stage of experimentation, multi-script signatures were identified based on the script type, and subsequently the verification task was explored separately for English, Hindi and Bangla signatures based on the identified script result. The gradient and chain code features were employed, and Support Vector Machines (SVMs) along with the Modified Quadratic Discriminate Function (MQDF) were considered in this scheme. From the experimental results achieved, it is noted that the verification accuracy obtained in the second stage of experiments (where a signature script identification method was introduced) is better than the verification accuracy produced following the first stage of experiments. Experimental results indicated that an average error rate of 20.80% and 16.40% were obtained for two different types of verification experiments.

关键词： Support vector machines

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Bag-of-features HMMs for segmentation-free Bangla word spotting 13

Bag-of-features HMMs for segmentation-free Bangla word spott...

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4th International Workshop on Multilingual OCR, MOCR 2013

作者： Rothacker, L. Fink, G.A. Banerjee, P. Bhattacharya, U. Chaudhuri, B.B. Department of Computer Science TU Dortmund University 44221 Dortmund Germany Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata-700 0108 India

ISBN: (纸本)9781450321143

In this paper we present how Bag-of-Features Hidden Markov Models can be applied to printed Bangla word spotting. These statistical models allow for an easy adaption to different problem domains. This is possible due to the integration of automatically estimated visual appearance features and Hidden Markov Models for spatial sequential modeling. In our evaluation we are able to report high retrieval scores on a new printed Bangla dataset. Furthermore, we outperform state-of-the-art results on the well-known George Washington word spotting benchmark. Both results have been achieved using an almost identical parametric method configuration. © 2013 ACM.

关键词： Hidden Markov models

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A new method for sclera vessel recognition using OLBP

引用

2012 International Conference on Service-Oriented Computing, ICSOC 2012

作者： Das, Abhijit Pal, Umapada Ferrer Ballester, Miguel A. Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9783319029603

This paper proposes a new sclera vessel recognition technique. The vessel patterns of sclera are unique for each individual and this can be utilized to identify a person uniquely. In this research we have used a time adaptive active contour-based region growing technique for sclera segmentation. Prior to that, we have made some tonal and illumination correction to get a clearer sclera area without the distributing vessel structure. This is because the presence of complex vessel structures occasionally affects the region-growing process. The sclera vessels are not prominent in the images, so in order to make them clearly visible, a local image enhancement process using a Haar high pass filter is incorporated. To get the total orientation of the vessels, we have used Orientated Local Binary pattern (OLBP). The OLBP images of each class are used for template matching for classification by calculating the minimum Hamming Distance. We have used the UBIRIS version 1 dataset for the experimentation of our research. The proposed approach has achieved high recognition accuracy employing the above-mentioned dataset. © Springer International Publishing 2013.

关键词： Local binary pattern

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SVM and nn based offline signature verification

SVM and nn based offline signature verification

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作者： Pal, Srikanta Alaei, Alireza Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Gold Coast Griffith University Australia Polytech'Tours Université François-Rabelais de Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal verification. The objective of this paper is to present an empirical contribution toward the understanding of a threshold-based signature verification technique involving off-line Bangla (Bengali) signatures. Experiments on signature verification concerning non-English signatures are an important consideration in the signature verification area. Only very few research works employing signatures of Indian script have been considered in the field of non-English based signature verification. To fill this gap, a threshold-based scheme for the verification of off-line Bangla signatures is proposed. Some techniques such as under-sampled bitmap, intersection/end point and directional chain code are employed for feature extraction. The thresholds are computed based on the similarity measures obtained employing the nearest neighbor classifier. The SVM classifier has also been considered for mainly comparative experimental result generation. Furthermore, a Bangla signature database, which consists of 2400 (100 × 24) genuine signatures and 3000 (100 × 30) forgeries, has been created and is employed for experimentation. An average error rate (AER) of 12.33% was obtained as the best verification result using directional chain code features in this research work. As a comparative study, a different dataset (GPDS-160) has also been considered. © 2013 Imperial College Press.

关键词： Codes (symbols)

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An approach for Bangla and Devanagari video text recognition 13

An approach for Bangla and Devanagari video text recognition

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4th International Workshop on Multilingual OCR, MOCR 2013

作者： Banerjee, Purnendu Chaudhuri, B.B. Society for Natural Language Technology Research Module 130 SDF Building Kolkata-700091 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata- 108 India

ISBN: (纸本)9781450321143

Extraction and recognition of Bangla text from video frame images is challenging due to fonts type and style variation, complex color background, low-resolution, low contrast etc. In this paper, we propose an algorithm for extraction and recognition of Bangla and Devanagari text form video frames with complex background. Here, a two-step approach has been proposed. After text localization, the text line is segmented into words using information based on line contours. First order gradient values of the text blocks are used to find the word gap. Next, an Adaptive SIS binarization technique is applied on each word. Next this binarized text block is sent to a state of the art OCR for recognition. © 2013 ACM.

关键词： Extraction

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Extraction of line-word-character segments directly from run-length compressed printed text-documents

Extraction of line-word-character segments directly from run...

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National Conference on computer vision, pattern recognition, Image Processing and Graphics (NCVPRIPG)

作者： Mohammed Javed P. Nagabhushan B.B. Chaudhuri Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Segmentation of a text-document into lines, words and characters, which is considered to be the crucial preprocessing stage in Optical Character recognition (OCR) is traditionally carried out on uncompressed documents, although most of the documents in real life are available in compressed form, for the reasons such as transmission and storage efficiency. However, this implies that the compressed image should be decompressed, which indents additional computing resources. This limitation has motivated us to take up research in document image analysis using compressed documents. In this paper, we think in a new way to carry out segmentation at line, word and character level in run-length compressed printed-text-documents. We extract the horizontal projection profile curve from the compressed file and using the local minima points perform line segmentation. However, tracing vertical information which leads to tracking words-characters in a run-length compressed file is not very straight forward. Therefore, we propose a novel technique for carrying out simultaneous word and character segmentation by popping out column runs from each row in an intelligent sequence. The proposed algorithms have been validated with 1101 text-lines, 1409 words and 7582 characters from a data-set of 35 noise and skew free compressed documents of Bengali, Kannada and English Scripts.

关键词： Image coding Image segmentation Optical character recognition software Text analysis Image analysis Facsimile

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Bengali Noun Morphological Analyzer

Bengali Noun Morphological Analyzer

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International Conference on Advances in Computing, Communications and Informatics

作者： Priyanka Das Arjun Das Dept. of Computational Linguistics The English and Foreign Languages University Computer Vision and Pattern Recognition Unit Indian statistical Institute

ISBN: (纸本)9781467361262

This paper describes a linguistics approach towards development of a Bengali Noun Morphological Analyzer implemented at first on the semi-manually created database of 87697 inflected words list tokens, i.e. Input2 for Linguistics Resource Creation comprising of Noun, Pronoun, Adjective roots with and without its suffixes. Then after the first implementation the developed Linguistic Resource knowledge is applied on an unknown Bengali corpus database containing 6157 tokens. At the initial stage of this research a linguistic analysis is done which leads to framing of the nominal suffix list which is later on used in nominal suffix extraction. This linguistic knowledge is implemented in developing the finite-state transducer grammar for Linguistic Resource which gives way to the development of Bengali Noun Morphological Analyzer. The final output obtained is around 44% accuracy. This accuracy can be always improved with time if we keep on increasing the nominal roots in the FST grammar file.

关键词： Morphological Analyzer Finite-State Transducer Grammar Nominal Suffix Xerox Finite-State Tool Compiler Lookup Tool

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ISIgraphy: A tool for online handwriting sample database generation

ISIgraphy: A tool for online handwriting sample database gen...

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National Conference on computer vision, pattern recognition, Image Processing and Graphics (NCVPRIPG)

作者： Arindam Das Ujjwal Bhattacharya Department of Computer Science & Engineering Narula Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute (ISI) Kolkata India

Online handwriting recognition research has recently received significant thrust. Specifically for Indian scripts, handwriting recognition has not been focused much till in the near past. However, due to generous Government funding through the group on Technology Development for Indian Languages (TDIL) of the Ministry of Communication & Information Technology (MC&IT), Govt. of India, research in this area has received due attention and several groups are now engaged in research and development works for online handwriting recognition in different Indian scripts. An extensive bottleneck of the desired progress in this area is the difficulty of collection of large sample databases of online handwriting in various scripts. Towards the same, recently a user-friendly tool on Android platform has been developed to collect data on handheld devices. This tool is called ISIgraphy and has been uploaded in the Google Play for free download. This application is designed well enough to store handwritten data samples in large scales in user-given file names for distinct users. Its use is script independent, meaning that it can collect and store handwriting samples written in any language, not necessarily an Indian script. It has an additional module for retrieval and display of stored data. Moreover, it can directly send the collected data to others via electronic mail.

关键词： Databases Handwriting recognition Character recognition Handheld computers Benchmark testing Text recognition

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Semi-supervised Online Learning of Handwritten Characters Using a Bayesian Classifier

Semi-supervised Online Learning of Handwritten Characters Us...

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Asian Conference on pattern recognition (ACPR)

作者： Rituraj Kunwar Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper addresses the problem of creating a handwritten character recognizer, which makes use of both labelled and unlabelled data to learn continuously over time to make the recognisor adaptable. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for better parameter estimation, especially when labelled data is scarce and expensive unlike unlabelled data. We introduce an algorithm for learning from labelled and unlabelled samples based on the combination of novel online ensemble of the Randomized Naive Bayes classifiers and a novel incremental variant of the Expectation Maximization (EM) algorithm. We make use of a weighting factor to modulate the contribution of unlabelled data. An empirical evaluation of the proposed method on Tamil handwritten base character recognition proves efficacy of the proposed method to carry out incremental semi-supervised learning and producing accuracy comparable to state-of-the-art batch learning method. Online handwritten Tamil characters from the IWFHR 2006 competition dataset was used for evaluating the proposed method.

关键词： Accuracy Training Handwriting recognition Character recognition Semisupervised learning Hidden Markov models

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