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Effect of perceptual anchorage points on recognition of Bangla characters

Effect of perceptual anchorage points on recognition of Bang...

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1st Indo-Japan Conference on Perception and Machine Intelligence, PerMIn 2012

作者： Bandyopadhyay, Asok Mukherjee, Bhaswati Chaudhuri, Bidyut Baran Centre for Development of Advanced Computing Plot-E2/1 Block GP Salt Lake Kolkata-700091 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata-700108 India

ISBN: (纸本)9783642273865

Character recognition (Printed and Handwritten) system has become an extremely useful tool in Human computer Interaction. Handwriting is a complex perceptual motor task generating linguistic information. Characters reflect shape distinction needed to perceive different phonetic information of words. We have explored 'perceptual processes' of character recognition for developing a cognitive model. Especially, we tried to extract the Perceptual Anchorage Points in the character. An experiment was performed to identify Perceptual anchorage points in a character both for handwritten as well as printed character of different Bangla script fonts including fonts used in Bangladesh. A set of important points and shapes has been found in the experiment. It is noted that deletion of these regions from characters greatly reduces the human cognition of characters. © 2012 Springer-Verlag.

关键词： Character recognition

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Fast and efficent multimodal eye biometrics using projective dictionary pair learning

Fast and efficent multimodal eye biometrics using projective...

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2016 IEEE Congress on Evolutionary Computation, CEC 2016

作者： Das, Abhijit Mondal, Prabir Pal, Umapada Ferrer, Miguel Angel 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 School of Software University of Technology Sydney Australia

ISBN: (纸本)9781509006229

This work proposes a projective pairwise dictionary learning-based approach for fast and efficient multimodal eye biometrics. The work uses a faster Projective pairwise Discriminative Dictionary Learning (DL) in contrast to the traditional DL which uses synthesis DL. Projective Pairwise Discriminative Dictionary (PPDD) uses a synthesis dictionary and an analysis dictionary jointly to achieve the goal of pattern representation and discrimination. As the PPDD process of DL is in contrast to the use of l0 or l1-norm sparsity constraints on the representation coefficients adopted in most traditional DL, it works faster than other DL. Moreover, the blending of synthesis dictionary and an analysis dictionary also enhance the feature representation of the complex eye patterns. We employed the combination of sclera and iris traits to establish multimodal biometrics. The experimental study and analysis conducted fulfill the hypothesis we considered. In this work we employed a part of the UBIRIS version 1 dataset to conduct the experiments. © 2016 IEEE.

关键词： Biometrics

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recognition of Multi-oriented Touching Characters in Graphical Documents

Recognition of Multi-oriented Touching Characters in Graphic...

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indian Conference on computer vision, Graphics & Image Processing (ICVGIP)

作者： Partha Pratim Roy Umapada Pal Josep Lladós Computer Vision Center Universitat Autònoma Barcelona Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Touching characters are major problem of achieving higher recognition rate in optical character recognition (OCR). Present OCR systems do not perform well when adjacent characters touch. If characters are touched in graphical documents (e.g. map) then such touching string recognition is more difficult because in such documents touching characters appear in multi-oriented direction. In this paper, we present a scheme towards the recognition of English two-character multi-oriented touching strings. When two or more characters touch, they generate a big cavity region at the background portion and we used this background information in our scheme. To handle the background information, convex hull is used. In this scheme, at first, a set of initial segmentation points is predicted based on the concave residues of the convex hull of the touching characters. Next, based on the initial points, we select some candidate segmentation lines. Finally the recognition confidence of two sub-images of a touching string, obtained from each candidate segmentation line is computed. The candidate segmentation line from which we get optimum confidence is the actual segmentation line and the corresponding characters in favour of which the two segmentation parts show optimum confidence is the recognition result of the touching string. To compute the recognition confidence, SVM classifier is used. The features used in the SVM are invariant to character orientation. Circular ring and convex hull ring based approach has been used along with angular information of the contour pixels of the character to make the feature rotation invariant. From the experiment we obtained encouraging result.

关键词： Character recognition Optical character recognition software pattern recognition Support vector machines Support vector machine classification Image segmentation Frequency Image recognition Character generation Degradation

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A modified approach of identification and tracking of heart boundary from MRI image sequences

A modified approach of identification and tracking of heart ...

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IEEE Region 10 International Conference TENCON

作者： B.B. Chaudhuri Sucharita Mitra Arindam Bhattacharya Sutama Mitra Saugata Dutta Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India Department of Computer Science and Engineering Heritage Institute of Technology India

In the present work, a robust algorithm for automatic identification and segmentation of heart portion from cardiac Magnetic Resonance video Image (MRI) is presented. At first, an outline has been generated to get the region of interest (ROI) by employing the moving object criterion, which eventually reduces the processing time significantly. In the next step, Expectation Maximization (EM) algorithm is used to segment the grey scale images into 5 distinct regions. This is done to make them more suitable for further processing and easy to use in the developed software. Finally Level set algorithm added with automatic contour generation module is used for tracking the exact heart boundary to segment it out from the rest of the image. This algorithm gives equally persistent result for both long axis and shot axis cardiac MRI data consisting of a movie (in AVI format) containing 32 separate frames of grayscale images.

关键词： Heart Magnetic resonance imaging Image sequences Image segmentation Deformable models Shape Level set Biomedical imaging Noise shaping Gray-scale

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Segmentation-Free Keyword Spotting for Bangla Handwritten Documents

Segmentation-Free Keyword Spotting for Bangla Handwritten Do...

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International Workshop on Frontiers in Handwriting recognition

作者： Xi Zhang Umapada Pal Chew Lim Tan School of Computing National University of Singapore Singapore Computer Vision And Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, a segmentation-free keyword spotting method is proposed for Bangla handwritten documents. In order to tolerate large variations in handwritten scenarios, we extracted key points based on SIFT key point detector, and the end and intersection points found by morphological operations. Heat Kernel signature (HKS) is used to present the local characteristics of detected key points. Instead of using the same size of patch for all the key points, we apply a method dynamically deciding the patch size. Furthermore, our spotting method reduces the scope of searching on the document by only considering the candidate local zones with similar candidate key points, and does not need pre-processing steps. From the experiment on Bangla handwritten text we obtained encouraging results.

关键词： Feature extraction Entropy Hidden Markov models Detectors Heating Kernel Indexes

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A New Deep Fuzzy Based MSER Model for Multiple Document Images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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3rd International Conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： Image classification

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Shape Code Based Word-Image Matching for Retrieval of indian Multi-lingual Documents

Shape Code Based Word-Image Matching for Retrieval of Indian...

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International Conference on pattern recognition

作者： Arundhati Tarafdar Ranju Mondal Srikanta Pal Umapada Pal Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9781424475421

In the current scenario retrieving information from document images is a challenging problem. In this paper we propose a shape code based word-image matching (word-spotting) technique for retrieval of multilingual documents written in indian languages. Here, each query word image to be searched is represented by a primitive shape code using (i) zonal information of extreme points (ii) vertical shape based feature (iii) crossing count (with respect to vertical bar position) (iv) loop shape and position (v) background information etc. Each candidate word (a word having similar aspect ratio and topological feature to the query word) of the document is also coded accordingly. Then, an inexact string matching technique is used to measure the similarity between the primitive codes generated from the query word image and each candidate word of the document with which the query image is to be searched. Based on the similarity score, we retrieve the document where the query image is found. Experimental results on Bangla, Devnagari and Gurumukhi scripts document image databases confirm the feasibility and efficiency of our proposed approach.

关键词： Shape Encoding Image coding Image segmentation Feature extraction pattern recognition computer vision

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A New Symmetry Based on Proximity of Wavelet-Moments for Text Frame Classification in Video

A New Symmetry Based on Proximity of Wavelet-Moments for Tex...

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International Conference on pattern recognition

作者： Palaiahnakote Shivakumara Anjan Dutta Chew Lim Tan Umapada Pal School of Computing National University of Singapore Singapore Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper proposes the use of a new symmetry property based on proximity of the median moments in the wavelet domain. The method divides a given frame into 16 equally sized blocks to classify the true text frame. The average of high frequency subbands of a block is used for computing median moments to brighten the text pixel in a block of video frame. Then K-means clustering with K=2 is applied on the median moments of the block to classify it as a probable text block. For classified blocks, average wavelet median moments are computed for a sliding window. We introduce Max-Min cluster to classify the probable text pixel in each probable text block. The four quadrants are formed from the centroid of the probable text pixels. The new concept called symmetry is introduced to identify the true text block based on proximity between probable text pixels in each quadrant. If the frame produces at least one true text block, it is considered as a text frame otherwise a non-text frame. The method is tested on three datasets to evaluate the robustness of the method in classification of text frames in terms of recall and precision.

关键词： Pixel Chromium Feature extraction pattern recognition Robustness Silicon Classification algorithms

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An efficient algorithm for detection of road-like structures in satellite images

An efficient algorithm for detection of road-like structures...

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International Conference on pattern recognition

作者： A. Mukherjee S.K. Parui D. Chaudhuri B.B. Chaudhuri R. Krishnan Indian Statistical Institute Computer Vision and Pattern Recognition Unit Calcutta India Advanced Data Processing Research Institute Secunderabad India

Road networks are important features of satellite imagery. The main contribution of the present road detection method consists of an effective enhancement technique and an efficient segmentation technique that removes non-road pixels step by step from the image where parameters involved: in each step images are determined by the sensor characteristics (like spatial resolution and spectral range) of the satellite. Also, the segmentation process depends not only on the road contrast but also on the road length. Thus, a low contrast but long road segment does not get removed. We have tested the algorithm on a number of images from IRS and SPOT satellites and the results are satisfactory.

关键词： Satellites Roads Image segmentation Pixel Sensor phenomena and characterization Spatial resolution Joining processes Intelligent networks computer vision pattern recognition

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A complete automatic short answer assessment system with student identification

A complete automatic short answer assessment system with stu...

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International Conference on Document Analysis and recognition

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

There are only a few studies undertaken in developing automatic assessment systems using handwriting recognition, even though a successful system would undoubtedly benefit the education system as schools and universities in many countries still employ paper-based examinations. To the best of the authors' knowledge, there is no existing work on an automatic off-line short answer assessment system comprising a student identification component. Hence in this paper, the authors propose a system towards this, where a new feature extraction technique called the Enhanced Water Reservoir, Loop and Gaussian Grid Feature, as well as other enhanced feature extraction techniques were utilised. Artificial Neural Networks and Support Vector Machines were employed as the classifiers; they were used for the investigation, and a comparison of the recognition and accuracy rates of the proposed systems, as well as the feature extraction techniques, was undertaken. The proposed assessment system achieved a recognition rate of 87.12% with 91.12% assessment accuracy, and the student identification component obtained a recognition rate of 99.52% with a 100% identification accuracy rate.

关键词： Feature extraction Field effect transistors Erbium

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