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作者： Garain, Utpal Halder, Biswajit Computer Vision and Pattern Recognition Unit and Centre for Artificial Intelligence and Machine Learning Indian Statistical Institute 203 B. T. Road Kolkata700108 India Department of CSE Narula Institute of Technology Agarpara Kolkata700109 India

With the emergence of big data, deep learning (DL) approaches are becoming quite popular in many branches of science. Forensic science is no longer an exception. However, there are certain problems in forensic science where the solutions would hardly benefit from the recent advances in DL algorithms. Document authentication is one such problem where we can have many reference samples, and with the big data scenario probably we would have even more number of reference samples but number of defective or forged samples will remain an issue. Experts often encounter situations where there is no or hardly a scanty number of forged samples available. In such situation, employment of data-hungry algorithms would be inefficient as they will not be able to learn the forged samples properly. This paper addresses this problem and proposes a novel reference modelling framework for forensic document authentication. The approach is based on Mahalanobis space. Two questioned document examination problems have been studied to show the effectiveness of our reference modelling algorithm which has also been compared to a commonly used learning approach, namely neural network-based classification. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Big data

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Stroke Segmentation and recognition from Bangla Online Handwritten Text

Stroke Segmentation and Recognition from Bangla Online Handw...

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International Workshop on Frontiers in Handwriting recognition

作者： Nilanjana Bhattacharya Umapada Pal Bose Institute Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper deals with recognition of online handwritten Bangla (Bengali) text. Here, at first, we segment cursive words into strokes. A stroke may represent a character or a part of a character. We selected a set of Bangla words written by different groups of people such that they contain all basic characters, all vowel and consonant modifiers and almost all types of possible joining among them. For segmentation of text into strokes, we discovered some rules analyzing different joining patterns of Bangla characters. Combination of online and offline information was used for segmentation. We achieved correct segmentation rate of 97.89% on the dataset. We manually analyzed different strokes to create a ground truth set of distinct stroke classes for result verification and we obtained 85 stroke classes. Directional features were used in SVM for recognition and we achieved correct stroke recognition rate of 97.68%.

关键词： Support vector machines Handwriting recognition Image segmentation Gold Writing Feature extraction Character recognition

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Off-line English and Chinese Signature Identification Using Foreground and Background Features

Off-line English and Chinese Signature Identification Using ...

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International Joint Conference on Neural Networks

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

ISBN: (纸本)9781467314886

In the field of information security, the usage of biometrics is growing for user authentication. Automatic signature recognition and verification is one of the biometric techniques, which is only one of several used to verify the identity of individuals. In this paper, a foreground and background based technique is proposed for identification of scripts from bi-lingual (English/Roman and Chinese) off-line signatures. This system will identify whether a claimed signature belongs to the group of English signatures or Chinese signatures. The identification of signatures based on its script is a major contribution for multi-script signature verification. Two background information extraction techniques are used to produce the background components of the signature images. Gradient-based method was used to extract the features of the foreground as well as background components. Zernike Moment feature was also employed on signature samples. Support Vector Machine (SVM) is used as the classifier for signature identification in the proposed system. A database of 1120 (640 English+480 Chinese) signature samples were used for training and 560 (320 English+240 Chinese) signature samples were used for testing the proposed system. An encouraging identification accuracy of 97.70% was obtained using gradient feature from the experiment.

关键词： Off-line verification systems Signature identification Biometrics Authentication systems SVM

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Off-line Handwritten Thai Name recognition for Student Identification in an Automated Assessment System

Off-line Handwritten Thai Name Recognition for Student Ident...

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International Joint Conference on Neural Networks

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

ISBN: (纸本)9781479914821

In the field of pattern recognition, off-line handwriting recognition is one of the most intensive areas of study. This paper proposes an automatic off-line Thai language student name identification system which was built as a part of a completed off-line automated assessment system. There is limited work undertaken in developing off-line automatic assessment systems using handwriting recognition. To the authors' knowledge, none of the work on the proposed system has been performed on the Thai language. In addition the proposed system recognises each Thai name by using an approach for whole word recognition, which is different from the work found in the literature as most perform character-based recognition. In this proposed system, the Gaussian Grid Feature (GGF) and the Modified Direction Feature (MDF) extraction techniques are investigated on upper and lower contours, loops from full word contour images of each name sample, and artificial neural networks and support vector machine are used as classifiers. The encouraging recognition rates for both feature extraction techniques were achieved when applied on loop, upper and lower contour images (99.27% accuracy rate was achieved using MDF on artificial neural networks and 99.27% using GGF with a support vector machine classifier).

关键词： Off-line handwriting recognition Automated assessment system Student identification system Modified directional feature Gaussian grid feature

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Semi-automatic segmentation of tissue cells from confocal microscope images

Semi-automatic segmentation of tissue cells from confocal mi...

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International Conference on pattern recognition

作者： P.S.Umesh Adiga B.B. Chaudhuri K. Rodenacker Computer Vision & Pattern Recognition Unit Indian Statistical Institute Calcutta India Institute of Pathology GSF Munich Germany

We present a semi-automatic method for extracting the 3D boundary of the cells in a compact tissue cross-section photographed by a confocal microscope. The confocal microscope provides pictures at different depths of the cells which can be considered as the image slices of the tissue section. Segmentation of cell boundary from different image slices and combining them to obtain 3D surface automatically is a difficult task. We have developed an approach where given one segmented image slice, the other image slices can be automatically segmented in a layered approach. The idea is to use the information of the previous segmented image slice for segmenting the current image slice.

关键词： Image segmentation Microscopy Laser beams Image processing computer vision pattern recognition Pathology Lighting Hardware Laser noise

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A Study on Word-Level Multi-script Identification from Video Frames

A Study on Word-Level Multi-script Identification from Video...

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International Joint Conference on Neural Networks

作者： Nabin Sharma Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Computer Vision and Pattern Recognition Unit Indian Statistical Institute

ISBN: (纸本)9781479914821

The presence of multiple scripts in multi-lingual document images makes Optical Character recognition (OCR) of such documents a challenging task. Due to the unavailability of a single OCR system which can handle multiple scripts, script identification becomes an essential step for choosing the appropriate OCR. Although, there are various techniques available for script identification from handwritten and printed documents having simple backgrounds, however script identification from video frames has been seldom explored. Video frames are coloured and suffer from low resolution, blur, complex background and noise to mention a few, which makes the script identification process a challenging task. This paper presents a study of various combinations of features and classifiers to explore whether the traditional script identification techniques can be applied to video frames. A texture based feature namely, Local Binary pattern (LBP), Gradient based features namely, Histogram of Oriented Gradient (HoG) and Gradient Local Auto-Correlation (GLAC) were used in the study. Combination of the features with SVMs and ANNs where used for classification. Three popular scripts, namely English, Bengali and Hindi were considered in the present study. Due to the inherent problems with the video, a super resolution technique was applied as a pre-processing step. Experiments show that the GLAC feature has performed better than the other features, and an accuracy of 94.25% was achieved when testing on 1271 words from three different scripts. The study also reveals that gradient features are more suitable for script identification than the texture features when using traditional script identification techniques on video frames.

关键词： Video document analysis Script identification Word segmentation OCR

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Page layout analyser for multilingual indian documents

Page layout analyser for multilingual Indian documents

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Language Engineering Conference

作者： A.R. Chaudhuri A.K. Mandal B.B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

An advanced Optical Character recognition (OCR) system is equipped with the module of the page layout analyser. It separates textual zones from non-textual zones. It identifies textual blocks from multicolumn documents and groups them into homogenous regions in terms of geometric shape and spatial distribution. All existing OCR modules developed for various indian scripts can handle text only single-column documents. In this paper, a page, layout analyser that uses typical common features present in most of the indian scripts is introduced. A simple compatibility criterion that allows various degrees of homogeneity is defined. The page-analyser is robust in the sense that it can distinguish text regions from non-textual entities such as images, rulers, and noisy signals due to smudges and poor quality of the paper. Test results are shown in two most popular indian Scripts, Devnagari (Hindi) and Bangla.

关键词： Optical character recognition software Geometrical optics Optical sensors Character recognition Shape Robustness Testing

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Convolutional Neural Networks Exploiting Attributes of Biological Neurons

arXiv

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arXiv 2023年

作者： Singh, Neeraj Kumar Pal, Nikhil R. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

In this era of artificial intelligence, deep neural networks like Convolutional Neural Networks (CNNs) have emerged as front-runners, often surpassing human capabilities. These deep networks are often perceived as the panacea for all challenges. Unfortunately, a common downside of these networks is their"black-box" character, which does not necessarily mirror the operation of biological neural systems. Some even have millions/billions of learnable (tunable) parameters, and their training demands extensive data and time. Here, we integrate the principles of biological neurons in certain layer(s) of CNNs. Specifically, we explore the use of neuro-science-inspired computational models of the Lateral Geniculate Nucleus (LGN) and simple cells of the primary visual cortex. By leveraging such models, we aim to extract image features to use as input to CNNs, hoping to enhance training efficiency and achieve better accuracy. We aspire to enable shallow networks with a Push-Pull Combination of Receptive Fields (PP-CORF) model of simple cells as the foundation layer of CNNs to enhance their learning process and performance. To achieve this, we propose a two-tower CNN, one shallow tower and the other as ResNet 18. Rather than extracting the features blindly, it seeks to mimic how the brain perceives and extracts features. The proposed system exhibits a noticeable improvement in the performance (on an average of 5% − 10%) on CIFAR-10, CIFAR-100, and ImageNet-100 datasets compared to ResNet-18. We also check the efficiency of only the Push-Pull tower of the network. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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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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Skeletal shape extraction from dot patterns by self-organization

Skeletal shape extraction from dot patterns by self-organiza...

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International Conference on pattern recognition

作者： A. Datta S.M. Parui B.B. Chaudhuri Computer and Statistical Service Centre Indian Statistical Institute Calcutta India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Calcutta India

Extraction of skeletal shape from a 2D dot pattern is discussed. We use a self-organizing neural network model to get a piecewise linear approximation of a skeleton of the pattern. It is found that even without a proper definition of a skeleton, the proposed algorithm is able to produce skeletons that are quite close to what we intuitively feel it should be. In Kohonen's self-organizing model, the set of processors and their neighbourhoods are fixed. We suggest here some modifications of it in which the set of processors and their neighbourhoods change adaptively.

关键词： Shape US Department of Transportation Skeleton Neural networks Network topology pattern recognition Piecewise linear approximation pattern analysis computer networks

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