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International Workshop on Frontiers in Handwriting recognition

作者： Sauradip Nag Palaiahnakote Shivakumara Yirui Wu Umapada Pal Tong Lu Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia College of Computer and Information Hohai University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, uses tangent angle of the contour pixels in each row and the mean of intensity values of each row for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method.

关键词： Feature extraction Shape Image edge detection Writing Image segmentation Forensics Support vector machines

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An end-to-end system for bangla online handwriting recognition 15

An end-to-end system for bangla online handwriting recogniti...

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15th International Conference on Frontiers in Handwriting recognition, ICFHR 2016

作者： Bhattacharya, Soumik Maitra, Durjoy Sen Bhattacharya, Ujjwal Parui, Swapan K. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781509009817

A few studies of online Bangla handwriting recognition such as isolated character recognition or limited vocabulary cursive word recognition are found in the literature. However, development of an end-to-end recognition system of unconstrained online Bangla handwritten texts has not been duly attempted so far. In the present report, we describe a similar system which takes a piece of continuous online handwritten Bangla texts as the input. It first segments the input texts into individual lines, each line into its constituent words and each word into sub-strokes. In the present study, 152 different symbols which include basic characters, character modifiers, frequently used conjunct characters, a few special characters and numerals have been considered. The entire set of sub-strokes obtained from the training sample set has been exhaustively studied by 3 experts and 76 different shapes of sub-strokes have been identified based on consensus among these experts. Also, it has been observed that a character may produce at most 3 sub-strokes. Since a piece of Bangla texts often contains either Bangla or English numerals, the present character set consists of both the numeral set and 3 numeral shapes are common to both the scripts. The proposed recognition system uses two classifiers, one for characters and the other for sub-strokes. Sub-strokes are fed to the character classifier in their temporal order. A single sub-stroke followed by two consecutive sub-strokes and finally three successive sub-strokes are passed to the character classifier and the first two top responses of the character classifier among the three cases are compared. If the difference is less than a threshold, the response of sub-stroke classifier is used to reach a final decision. The proposed system provided 94.3% character level accuracy on a test set consisting of 33,453 word samples written by 31 writers. © 2016 IEEE.

关键词： Character recognition

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Text Independent Writer Identification for Telugu Script Using Directional Filter Based Features

Text Independent Writer Identification for Telugu Script Usi...

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International Conference on Document Analysis and recognition

作者： Chris Andrew Santhoshini Reddy Viswanath Pulabaigari Umapada Pal International Institute of Information Technology Bangalore Karnataka IN Indian Institute of Information Technology Sri City Andhra Pradesh India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata West Bengal IN

In this paper, a novel approach for writer identification from handwritten Telugu script is introduced. A new dataset of Telugu handwritten documents of 150 writers called the IIITS-THDB is created under this work for doing the experimental analysis and this dataset is made available publicly. Descriptive convolution based features are extracted from given handwritten text using directional filters. Feature reduction is performed to find the optimal linear subspace for the extracted features and identification is done using these reduced features. Smaller blocks of text with uniform textures are created using the available data that show an improved identification accuracy during experimentation. We also study the effects of noise and compare the results of our features with various other features over our dataset.

关键词： Feature extraction Kernel Convolution Writing Junctions Zirconium Image edge detection

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Local jet pattern: A robust descriptor for texture classification

arXiv

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arXiv 2017年

作者： Roy, Swalpa Kumar Chanda, Bhabatosh Chaudhuri, Bidyut B. Ghosh, Dipak Kumar Dubey, Shiv Ram Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata700108 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India Department of Electronics & Communication Engineering National Institute of Technology Rourkela Orissa769008 India Computer Vision Group Indian Institute of Information Technology Sri City Andhra Pradesh517646 India

Methods based on locally encoded image features have recently become popular for texture classification tasks, particularly in the existence of large intra-class variation due to changes in illumination, scale, and viewpoint. Inspired by the theories of image structure analysis, this work proposes an efficient, simple, yet robust descriptor namely local jet pattern (LJP) for texture classification. In this approach, a jet space representation of a texture image is computed from a set of derivatives of Gaussian (DtGs) filter responses up to second order, so-called local jet vectors (LJV), which also satisfy the Scale Space properties. The LJP is obtained by using the relation of center pixel with its' local neighborhoods in jet space. Finally, the feature vector of a texture image is formed by concatenating the histogram of LJP for all elements of LJV. All DtGs responses up to second order together preserves the intrinsic local image structure, and achieves invariance to scale, rotation, and reflection. This allows us to design a discriminative and robust framework for texture classification. Extensive experiments on five standard texture image databases, employing nearest subspace classifier (NSC), the proposed descriptor achieves 100%, 99.92%, 99.75%, 99.16%, and 99.65% accuracy for Outex_TC10, Outex_TC12, KTH-TIPS, Brodatz, CUReT, respectively, which are better compared to state-of-the-art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Textures

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2018年 703卷 v-vi页

作者： Chaudhuri, Bidyut B. Kankanhalli, Mohan S. Raman, Balasubramanian Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Computing National University of Singapore Singapore Singapore Department of Computer Science and Engineering Indian Institute of Technology Roorkee RoorkeeUttarakhand India

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SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition

SSERBC 2017: Sclera segmentation and eye recognition benchma...

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2017 IEEE International Joint Conference on Biometrics, IJCB 2017

作者： Das, Abhijit Pal, Umapada Ferrer, Miguel A. Blumenstein, Michael Štepec, Dejan Rot, Peter Emeršič, Žiga Peer, Peter Štruc, Vitomir Kumar, S.V. Aruna Harish, B.S. 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 Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Department of Information Science and Engineering Sri Jayachamrajendra College of Engineering Mysusru Karnataka India

ISBN: (纸本)9781538611241

This paper summarises the results of the Sclera Segmentation and Eye recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject. In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82×2) eyes. A manual segmentation mask of these images was created to baseline both tasks. Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and peri-ocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems. The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject. © 2017 IEEE.

关键词： Competition

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SigNet: Convolutional siamese network for writer independent offline signature verification

arXiv

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arXiv 2017年

作者： Dey, Sounak Dutta, Anjan Ignacio Toledo, J. Ghosh, Suman K. Lladós, Josep Pal, Umapada Computer Vision Center Computer Science Dept. Universitat Autònoma de Barcelona Edifici O Campus Uab Bellaterra08193 Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata700108 India

Offline signature verification is one of the most challenging tasks in biometrics and document foren-sics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might only differ from a real signature by some specific kinds of deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to handle forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, provided better results than the state-of-the-art results on most of the benchmark signature datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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Product graph-based higher order contextual similarities for inexact subgraph matching

arXiv

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arXiv 2017年

作者： Dutta, Anjan Lladós, Josep Bunke, Horst Pal, Umapada Computer Vision Center Universitat Autònoma de Barcelona Edifici O Campus UAB Bellaterra Barcelona08193 Spain Institute of Computer Science University of Bern Neubrückstrasse 10 BernCH-3012 Switzerland Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T.Road Kolkata-108 India

Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities add discriminating power and allow one to find approximate solutions to the subgraph matching problem. Copyright © 2017, The Authors. All rights reserved.

关键词： Graphic methods

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An Automatic Student Verification System Utilising Off-Line Thai Name Components

An Automatic Student Verification System Utilising Off-Line ...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Hemmaphan Suwanwiwat Abhijit Das Miguel A. Ferrer Umapada Pal Michael Blumenstein Information Technology Academy James Cook University Cairns Australia School of Software University of Technology Sydney Sydney Australia Palmas de Gran Canaria IDeTIC University of Las Las Palmas Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This research proposed an automatic student identification and verification system utilising off-line Thai name components. The Thai name components consist of first and last names. Dense texture-based feature descriptors were able to yield encouraging results when applied to different handwritten text recognition scenarios. As a result, the authors employed such features in investigating their performance on Thai name component verification system. In this research, Dense-Local Binary pattern, Dense-Local Directional pattern, and Local Binary pattern combined with Local Directional pattern were employed. A base-line shape/feature i.e. Hidden Markov Model (HMM) was also utilised in this study. As there is no dataset on Thai name verification in the literature, a dataset is proposed for a Thai name verification system. The name component samples were collected from high school students. It consists of 8,400 name components (first and last names) from 100 students. Each student provided 60 genuine name components, and each of the name components was forged by 12 other students. An encouraging result was found employing the above-mentioned features on the proposed dataset.

关键词： Hidden Markov models Feature extraction Handwriting recognition Australia Support vector machines Head

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An End-to-End System for Bangla Online Handwriting recognition

An End-to-End System for Bangla Online Handwriting Recogniti...

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International Workshop on Frontiers in Handwriting recognition

作者： Soumik Bhattacharya Durjoy Sen Maitra Ujjwal Bhattacharya Swapan K. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781509009824

关键词： Handwriting recognition Character recognition Shape Text recognition Support vector machines Vocabulary Trajectory

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