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检索条件"机构=Computer Vision and Pattern"

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F-ratio Based Weighted Feature Extraction for Similar Shape Character Recognition

F-ratio Based Weighted Feature Extraction for Similar Shape ...

引用

International Conference on Document Analysis and Recognition

作者： Tetsushi Wakabayashi Umapada Pal Fumitaka Kimura Yasuji Miyake Graduate School of Engineering Mie University Tsu Mie Japan Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781424445004

Recognition of handwritten similar shaped character is a difficult problem and in character recognition system most of the errors occur from similar shaped characters. In this paper we proposed a novel feature extraction technique to improve the recognition results of two similar shaped characters. The technique is based on F-ratio (Fisher Ratio), a statistical measure defined by the ratio to the between-class variance and within-class variance. F-ratio modifies the feature vector of two similar shape characters by weighting the feature elements. This weighting scheme enhances the feature elements that belongs to the distinguishable portions of the similar shaped characters and reduces the feature elements of the common portion of the characters, so that similar shaped characters can be identified easily. We considered pair of handwritten similar shape characters of different scripts like Arabic/Persian, Devnagari English, Bangla, Oriya, Tamil, Kannada, Telugu etc. and we noted that f-ratio based feature weighting shows better recognition results.

关键词： Feature extraction Shape Character recognition Handwriting recognition pattern recognition Text analysis pattern analysis computer vision computer errors Writing

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Comparative Study of Devnagari Handwritten Character Recognition Using Different Feature and Classifiers

Comparative Study of Devnagari Handwritten Character Recogni...

引用

International Conference on Document Analysis and Recognition

作者： Umapada Pal Tetsushi Wakabayashi Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering TSU Mie University Mie Japan

In recent years research towards Indian handwritten character recognition is getting increasing attention. Many approaches have been proposed by the researchers towards handwritten Indian character recognition and many recognition systems for isolated handwritten numerals/characters are available in the literature. To get idea of the recognition results of different classifiers and to provide new benchmark for future research, in this paper a comparative study of Devnagari handwritten character recognition using twelve different classifiers and four sets of feature is presented. Projection distance, subspace method, linear discriminant function, support vector machines, modified quadratic discriminant function, mirror image learning, Euclidean distance, nearest neighbour, k-Nearest neighbour, modified projection distance, compound projection distance, and compound modified quadratic discriminant function are used as different classifiers. Feature sets used in the classifiers are computed based on curvature and gradient information obtained from binary as well as gray-scale images.

关键词： Character recognition Handwriting recognition Shape pattern recognition Support vector machines Support vector machine classification Gray-scale Mirrors Machine learning Euclidean distance

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Handwritten Word Image Retrieval with Synthesized Typed Queries

Handwritten Word Image Retrieval with Synthesized Typed Quer...

引用

International Conference on Document Analysis and Recognition

作者： José A. Rodríguez-Serrano Florent Perronnin Computer Vision Centre Universitat Authnòma de Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France

We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained ldquoon the flyrdquo with images rendered from the searched words in one or multiple computer fonts. To reduce the mismatch between the typed-text prototypes and the candidate handwritten images, we make use of: (i) local gradient histogram(LGH) features, which were shown to model word shapes robustly, and (ii) semi-continuous hidden Markov models(SC-HMM), in which the typed-text models are constrained to a ldquovocabularyrdquo of handwritten shapes, thus learning a link between both types of data. Experiments show that the proposed method is effective in retrieving handwritten words, and the comparison to alternative methods reveals that the contribution of both the LGH features and the SCHMM is crucial. To the best of the authorspsila knowledge, this is the first work to address this issue in a non-trivial manner.

关键词： Image retrieval Hidden Markov models Prototypes Shape Robustness pattern analysis Handwriting recognition Rendering (computer graphics) Writing Text analysis

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Fisher Kernels for Handwritten Word-spotting

Fisher Kernels for Handwritten Word-spotting

引用

International Conference on Document Analysis and Recognition

作者： Florent Perronnin Jose A. Rodriguez-Serrano Textual and Visual Pattern Analysis Xerox Research Centre Europe France Computer Vision Centre Universitat Authnòma de Barcelona Spain

The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to apply this framework to handwritten word-spotting. Given a word image and a keyword generative model, the idea is to generate a vector which describes how the parameters of the keyword model should be modified to best fit the word *** vector can then be used as the input of a discriminative classifier. We compare the performance of the proposed approach with that of a generative baseline on a challenging real-world dataset of customer letters. When the kernel used by the classifier is linear, the performance improvement is marginal but the proposed system is approximately 15 times faster than the baseline. If we use a non-linear kernel devised for this task, we obtain a 15% relative reduction of the error but the detector is approximately 15 times slower.

关键词： Kernel Hidden Markov models pattern classification pattern analysis Europe Text analysis Handwriting recognition pattern recognition computer vision Vectors

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A Color Edge Detection Algorithm in RGB Color Space

A Color Edge Detection Algorithm in RGB Color Space

引用

International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom

作者： Soumya Dutta Bidyut B. Chaudhuri Department of Electronics and Communication Netaji Subhash Engineering College Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781424451043

Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A color image edge detection algorithm is proposed in this paper. Average maximum color difference value is used to predict the optimum threshold value for a color image and thinning technique is applied to extract proper edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.

关键词： Image edge detection Color Image processing Object recognition Shape measurement pattern recognition Area measurement computer vision Image segmentation Image coding

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A Statistics and Local Homogeneity Based Color Edge Detection Algorithm

A Statistics and Local Homogeneity Based Color Edge Detectio...

引用

International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom

Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A novel color edge detection algorithm is proposed in this paper. On the basis of standard deviation calculation of pixels the discontinuity among the pixels are detected. Then the image is segmented into a binary image with a fixed threshold where black pixels signify homogeneous region and white pixels signify edges. Finally, a thinning technique is applied to extract thin edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.

关键词： Statistics Image edge detection Image processing Object recognition Shape measurement Image segmentation Pixel pattern recognition Area measurement computer vision

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Machine Authentication of Security Documents

Machine Authentication of Security Documents

引用

International Conference on Document Analysis and Recognition

作者： Utpal Garain Biswajit Halder Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of IT Greater Kolkata College of Engineering and Management Baruipur West Bengal India

ISBN: (纸本)9781424445004

This paper presents a pioneering effort towards machine authentication of security documents like bank cheques, legal deeds, certificates, etc. that fall under the same class as far as security is concerned. The proposed method first computationally extracts the security features from the document images and then the notion of dasiagenuinepsila vs. dasiaduplicatepsila is defined in the feature space. Bank cheques are taken as a reference for conducting the present experiment. Support Vector Machines (SVMs) and neural networks (NN) are involved to verify authenticity of these cheques. Results on a test dataset of 200 samples show that the proposed approach achieves about 98% accuracy for discriminating duplicate cheques from genuine ones. This strongly attests the viability of involving machine in authenticating security documents.

关键词： Authentication Security Printing Ink pattern recognition Feature extraction Support vector machines Neural networks Law Legal factors

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Indian Multi-Script Full Pin-code String Recognition for Postal Automation

Indian Multi-Script Full Pin-code String Recognition for Pos...

引用

International Conference on Document Analysis and Recognition

作者： Umapada Pal Rami Kumar Roy Kaushik Roy Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Computer Science West Bengal State University Barasat India Graduate School of Engineering TSU Mie University Japan

ISBN: (纸本)9781424445004

Under three-language formula, the destination address block of postal document of an Indian state is generally written in three languages: English, Hindi and the State official language. Because of inter-mixing of these scripts in postal address writings, it is very difficult to identify the script by which a pin-code is written. Also, because of the writing style of different individuals some of the digits in a pin-code string may touch with its neighboring digits. Accurate segmentation of such touching components into individual digits is a difficult task. To avoid such difficulties, in this paper we proposed a tri-lingual (English, Hindi and Bangla) 6-digit full pin-code string recognition. We obtained 99.01% reliability from our proposed system when error and rejection rates are 0.83% and 15.27%, respectively.

关键词： Automation Natural languages pattern recognition Writing Statistics Optical character recognition software Text analysis pattern analysis computer vision Databases

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Robust recognition of 1-D barcodes using camera phones

Robust recognition of 1-D barcodes using camera phones

引用

作者： Wachenfeld, Steffen Terlunen, Sebastian Jiang, Xiaoyi Department of Computer Science Computer Vision and Pattern Recognition Group University of Munster Germany

ISBN: (纸本)9781424421756

In this paper we present an algorithm for the recognition of 1D barcodes using camera phones, which is highly robust regarding the the typical image distortions. We have created a database of barcode images, which covers typical distortions, such as inhomogeneous illumination, reflections, or blurriness due to camera movement. We present results from experiments with over 1,000 images from this database using a Matlab implementation of our algorithm, as well as experiments on the go, where a Symbian C++ implementation running on a camera phone is used to recognize barcodes in daily life situations. The proposed algorithm shows a close to 100% accuracy in real life situations and yields a very good resolution dependent performance on our database, ranging from 90.5% (640 × 480) up to 99.2% (2592 × 1944). The database is freely available for other researchers. © 2008 IEEE.

关键词： Database systems

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Issues in searching for indian language web content

Issues in searching for indian language web content

引用

2nd ACM Workshop on Improving Non English Web Searching, iNEWS'08, Co-located with the 17th ACM Conference on Information and Knowledge Management, CIKM 2008

作者： Pal, Dipasree Majumder, Prasenjit Mitra, Mandar Mitra, Sukanya Sen, Aparajita Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781605584164

This paper looks at the problem of searching for Indian language (IL) content on the Web. Even though the amount of IL content that is available on the Web is growing rapidly, searching through this content using the most popular websearch engines poses certain problems. Since the popular search engines do not use any stemming / orthographic normalization for Indian languages, recall levels for IL searches can be low. We provide some examples to indicate the extent of this problem, and suggest a simple and efficient solution to the problem. Copyright 2008 ACM.

关键词： Search engines

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