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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

ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Wakabayashi, T. Pal, U. Kimura, F. Miyake, Y. Graduate School of Engineering Mie University TSU 1577 Kurimamachiya-cho Mie 514-8507 Japan Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India

ISBN: (纸本)9780769537252

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. © 2009 IEEE.

关键词： Character recognition

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Devanagari and Bangla Text Extraction from Natural Scene Images

Devanagari and Bangla Text Extraction from Natural Scene Ima...

引用

International Conference on Document Analysis and recognition

作者： Ujjwal Bhattacharya Swapan Kumar Parui Srikanta Mondal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

With the increasing popularity of digital cameras attached with various handheld devices, many new computational challenges have gained significance. One such problem is extraction of texts from natural scene images captured by such devices. The extracted text can be sent to OCR or to a text-to-speech engine for recognition. In this article, we propose a novel and effective scheme based on analysis of connected components for extraction of Devanagari and Bangla texts from camera captured scene images. A common unique feature of these two scripts is the presence of headline and the proposed scheme uses mathematical morphology operations for their extraction. Additionally, we consider a few criteria for robust filtering of text components from such scene images. Moreover, we studied the problem of binarization of such scene images and observed that there are situations when repeated binarization by a well-known global thresholding approach is effective. We tested our algorithm on a repository of 100 scene images containing texts of Devanagari and / or Bangla.

关键词： Layout Handheld computers Digital cameras Optical character recognition software Speech synthesis Engines Text recognition Image analysis Morphology Robustness

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An anti-compression watermarking scheme for vector map based on improved Douglas-Peucker algorithm

An anti-compression watermarking scheme for vector map based...

引用

1st International Workshop on Education Technology and computer Science, ETCS 2009

作者： Zhang, Zuoli Wang, Yaming Sun, Shusen Research Institute of Computer Vision and Pattern Recognition ZheJiang Sci.-Tech. University Hangzhou China School of Information and Electronics ZheJiang Sci.-Tech. University Hangzhou China

ISBN: (纸本)9780769535579

An anti-compression watermarking scheme is proposed. The scheme uses improved Dougleas-Peucker algorithm to compress redundant vertex data, which is important character of vector map, and embeds the watermark into compressed vertex data. Experimental results show that the anticompression performance of the watermarking algorithm is satisfied. © 2009 IEEE.

关键词： pattern matching

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Gray-Level Image Segmentation Based on Markov Chain Monte Carlo

Gray-Level Image Segmentation Based on Markov Chain Monte Ca...

引用

International Workshop on Education Technology and computer Science, ETCS

作者： Wenqing Huang Kebiao Zheng Xiaokai Zhu The Institute of Computer Vision and Pattern Recognition Zhejiang Science and Technology University HangZhou China

A new method called Markov chain Monte Carlo (MCMC) is proposed for image segmentation. The MCMC method mainly contains three aspects. Firstly, the image segmentation problem is formulated in a Bayesian statistical framework. Four types of gray-level image models are set up. Secondly, the solution space is decomposed into a union of many subspaces. Thirdly, ergodic Markov chains are designed to explore the solution space and sample the posterior probability. We test the MCMC algorithm on a wide variety of gray-level images and some results are shown in the paper.

关键词： Image segmentation Monte Carlo methods Bayesian methods Space exploration computer vision Testing Deformable models Lattices computer science education Educational technology

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Using Modified Contour Features and SVM Based Classifier for the recognition of Persian/Arabic Handwritten Numerals

Using Modified Contour Features and SVM Based Classifier for...

引用

International Conference on Advances in pattern recognition (ICAPR)

作者： Alireza Alaei Umapada Pal P. Nagabhushan Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, we propose a robust and efficient feature set based on modified contour chain code to achieve higher recognition accuracy of Persian/Arabic numerals. In classification part, we employ support vector machine (SVM) as classifier. Feature set consists of 196 dimensions, which are the chain-code direction frequencies in the contour pixels of input image. We evaluated our scheme on 80,000 handwritten samples of Persian numerals. Using 60,000 samples for training, we tested our scheme on other 20,000 samples and obtained 98.71% correct recognition rate. Further, we obtained 99.37% accuracy using five-fold cross validation technique on 80,000 dataset.

关键词： Support vector machines Support vector machine classification Handwriting recognition pattern recognition Feature extraction Shape Frequency Pixel Writing Optical character recognition software

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Fine Classification of Unconstrained Handwritten Persian/Arabic Numerals by Removing Confusion amongst Similar Classes

Fine Classification of Unconstrained Handwritten Persian/Ara...

引用

International Conference on Document Analysis and recognition

作者： Alireza Alaei P. Nagabhushan Umapada Pal Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781424445004

In this paper, we propose two types of feature sets based on modified chain-code direction frequencies in the contour pixels of input image and modified transition features (horizontally and vertically). A multi-level support vector machine (SVM) is proposed as classifier to recognize Persian isolated digits. In first level, we combine similar shaped numerals into a single group and as result; we obtain 7 classes instead of 10 classes. We compute 196-dimension chain-code direction frequencies as features to discriminate 7 classes. In the second level, classes containing more than one numeral because of high resemblance in their shapes are considered. We use modified transition features (horizontally and vertically) for discriminating between two overlapping classes (0 and 1). To separate another overlapping group containing three numerals 2, 3 and 4 we first eliminate common parts of these digits (tail) and then compute chain code features. We employ SVM classifier for the classification and evaluate our scheme on 80,000 handwritten samples of Persian numerals [10]. Using 60,000 samples for training, we tested our scheme on other 20,000 samples and obtained 99.02% accuracy.

关键词： Support vector machines Support vector machine classification Shape Character recognition Handwriting recognition Frequency Natural languages pattern recognition Testing Text analysis

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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

ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Pal, U. Roy, R.K. Roy, K. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Dept. of Comp. Sc. West Bengal State University Barasat India Graduate School of Engg. Mie University 1577 Kurimamachiya-cho Tsu 514-8507 Japan

ISBN: (纸本)9780769537252

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. © 2009 IEEE.

关键词： Codes (symbols)

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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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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

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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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

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

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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