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10th International Conference on Information Technology, ICIT 2007

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

ISBN: (纸本)0769530680

In this paper we present a system towards the recognition of off-line Oriya handwritten characters. Since most of the Oriya characters have curve-like stroke, we use curvature feature for the recognition purpose. To get the feature, at first, the input image is size normalized and segmented into 4949 blocks. Curvature is then computed using bi-quadratic interpolation method and quantized into 3 levels according to concave, linear and convex regions. Next direction of gradient is quantized into 32 levels with π/16 intervals, and strength of the gradient is accumulated in each of the 32 directions and in each of the 3 curvature levels of every block. A spatial resolution is made to get 77 blocks from 4949 blocks and a directional resolution is made to get 8 directions from 32 directions. Using curvature features for 3 levels we get 1176 (77 blocks × 8 directions × 3 levels) dimensional features. Finally using principal component analysis we reduce the dimension 1176 to 392 and this 392 dimensional feature vector is fed to a quadratic classifier for recognition. We tested 18190 samples of Oriya handwritten samples and obtained 94.60% accuracy from our proposed system. © 2007 IEEE.

关键词： Principal component analysis

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A New Contrastive Learning Based Model for Estimating Degree of Multiple Personality Traits Using Social Media Posts 1

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7th Asian Conference on pattern recognition, ACPR 2023

作者： Biswas, Kunal Shivakumara, Palaiahnakote Pal, Umapada Sarkar, Ram Jadavpur University Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Estimating the degree of multiple personality traits in a single image is challenging due to the presence of multiple people, occlusion, poor quality etc. Unlike existing methods which focus on the classification of a single personality using images, this work focuses on estimating different personality traits using a single image. We believe that when the image contains multiple persons and modalities, one can expect multiple emotions and expressions. This work separates given input images into different faces of people, recognized text, meta-text and background information using face segmentation, text recognition and scene detection techniques. Contrastive learning is explored to extract features from each segmented region based on clustering. The proposed work fuses textual and visual features extracted from the image for estimating the degree of multiple personality traits. Experimental results on our benchmark datasets show that the proposed model is effective and outperforms the existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image segmentation

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Machine authentication of security documents

Machine authentication of security documents

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Garain, Utpal Halder, Biswajit Computer Vision and Pattern Recognition Unit India Statistical Institute 203 B.T. Road Kolkata 700108 India WB India

ISBN: (纸本)9780769537252

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 'genuine' vs. 'duplicate' 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. © 2009 IEEE.

关键词： Support vector machines

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Exploring the robustness of NMT systems to nonsensical inputs

arXiv

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arXiv 2019年

作者： Chaturvedi, Akshay Abijith, K.P. Garain, Utpal Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

Neural machine translation (NMT) systems have been shown to give undesirable translation when a small change is made in the source sentence. In this paper, we study the behaviour of NMT systems when multiple changes are made to the source sentence. In particular, we ask the following question "Is it possible for an NMT system to predict same translation even when multiple words in the source sentence have been replaced?". To this end, we propose a soft-attention based technique to make the aforementioned word replacements. The experiments are conducted on two language pairs: English- German (en-de) and English-French (en-fr) and two state-of-theart NMT systems: BLSTM-based encoder-decoder with attention and Transformer. The proposed soft-attention based technique achieves high success rate and outperforms existing methods like HotFlip by a significant margin for all the conducted experiments. The results demonstrate that state-of-the-art NMT systems are unable to capture the semantics of the source language. The proposed soft-attention based technique is an invariance-based adversarial attack on NMT systems. To better evaluate such attacks, we propose an alternate metric and argue its benefits in comparison with success rate. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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A probabilistic framework for logo detection and localization in natural scene images

A probabilistic framework for logo detection and localizatio...

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International Conference on pattern recognition

作者： Ankush Roy Utpal Garain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9781467322164

This paper presents a probabilistic approach for logo detection and localization in natural scene images. Two probability distributions are computed, one considering the features extracted from the key points located inside a region and the second refers to shape geometry defined by the key points. The barycentric co-ordinates are considered to define the shape statistics. The performance of the proposed approach has been reported on two publicly available datasets: BelgaLogos and Flickr Logos27. It is shown that statistically significant improvement is achieved over a recently proposed method.

关键词： Feature extraction Shape Geometry Accuracy Probabilistic logic Databases Visualization

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Bengali Place Name recognition - Comparative Analysis Using Different CNN Architectures 5th

Bengali Place Name Recognition - Comparative Analysis Using ...

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5th International Conference on computer vision and Image Processing, CVIP 2020

作者： Prasad, Prashant Kumar Banerjee, Pamela Chanda, Sukalpa Pal, Umapada RCC Institute of Information Technology Kolkata India Department of Information Technology Østfold University College Halden Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9789811610912

Optical Character recognition (OCR) has been deployed in the past in different application areas such as automatic transcription and indexing of document images, reading aid for the visually impaired persons, postal automation etc. However, the performance in many cases has not been impressive due to the fact that character segmentation is itself an error-prone and difficult operation, which leads to the poor performance of the system due to erroneous segmentation of characters. Hence, for many applications (like document indexing, Postal automation) where full character-wise transcription is not required, word recognition is the preferred method these days. This article investigates recognition of Bengali place names as word images using 5 different traditional architectures. Experiments on word images (of Bengali place names) from 608 classes were conducted. Encouraging results were obtained in all instances. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image recognition

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Proceedings of 2nd International Conference on computer vision & Image Processing 1

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丛书名： Advances in Intelligent Systems and Computing

2018年

作者： Bidyut B. Chaudhuri Mohan S. Kankanhalli Balasubramanian Raman

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Mimic and fool: a task agnostic adversarial attack

arXiv

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arXiv 2019年

作者： Chaturvedi, Akshay Garain, Utpal Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

At present, adversarial attacks are designed in a task-specific fashion. However, for downstream computer vision tasks such as image captioning, image segmentation etc., the current deep learning systems use an image classifier like VGG16, ResNet50, Inception-v3 etc. as a feature extractor. Keeping this in mind, we propose Mimic and Fool, a task agnostic adversarial attack. Given a feature extractor, the proposed attack finds an adversarial image which can mimic the image feature of the original image. This ensures that the two images give the same (or similar) output regardless of the task. We randomly select 1000 MSCOCO validation images for experimentation. We perform experiments on two image captioning models, Show and Tell, Show Attend and Tell and one VQA model, namely, end-to-end neural module network (N2NMN). The proposed attack achieves success rate of 74.0%, 81.0% and 89.6% for Show and Tell, Show Attend and Tell and N2NMN respectively. We also propose a slight modification to our attack to generate natural-looking adversarial images. In addition, it is shown that the proposed attack also works for invertible architecture. Since Mimic and Fool only requires information about the feature extractor of the model, it can be considered as a gray-box attack. Copyright © 2019, The Authors. All rights reserved.

关键词： Image segmentation

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Lexicon reduction technique for Bangla handwritten word recognition

Lexicon reduction technique for Bangla handwritten word reco...

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Bhowmik, Tapan Kumar Roy, Utpal Parui, Swapan K. Faculty of Mathematics and Natural Sciences University of Groningen Netherlands Department of Computer and System Sciences Visva Bharati Santiniketan India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9780769546612

In this paper we introduce a stroke based lexicon reduction technique in order to reduce the search space for recognition of handwritten words. The principle of this technique involves mainly two aspects of a word image to constitute a feature vector: one is word-length and the other is shape of the word. The length of the word image is represented by the number of specific vertical strokes present in the word image and, on the other hand, the shape of a word image is realized with the combination of both horizontal and vertical strokes. The experiment has been carried out with a database of 35,700 off-line handwritten Bangla word images. Though our proposed lexicon reduction technique is developed for recognition of Bangla handwritten words, its generalization property can easily be exploited for recognition of handwriting in other scripts also. © 2012 IEEE.

关键词： Character recognition

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Text independent writer identification for Bengali script

Text independent writer identification for Bengali script

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International Conference on pattern recognition

作者： Chanda, Sukalpa Franke, Katrin Pal, Umapada Wakabayashi, Tetsushi Department of Computer Science and Media Technology Gjøvik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9780769541099

Automatic identification of an individual based on his/her handwriting characteristics is an important forensic tool. In a computational forensic scenario, presence of huge amount of text/information in a questioned document cannot be always ensured. Also, compromising in terms of systems reliability under such situation is not desirable. We here propose a system to encounter such adverse situation in the context of Bengali script. Experiments with discrete directional feature and gradient feature are reported here, along with Support Vector Machine (SVM) as classifier. We got promising results of 95.19% writer identification accuracy at first top choice and 99.03% when considering first three top choices. © 2010 IEEE.

关键词： Support vector machines

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