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International Workshop on Frontiers in Handwriting recognition

作者： Rajmohan Pardeshi B.B. Chaudhuri Mallikarjun Hangarge K.C. Santosh Department of Computer Science Karnatak Arts Science & Commerce College Bidar INDIA Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata INDIA US National Library of Medicine (NLM) National Institutes of Health (NIH) Bethesda MD USA

Since OCR engines are usually script-dependent, automatic text recognition in multi-script document requires a pre-processor module that identifies the scripts. Based on this motivation, in this paper, we present a word level handwritten indian script identification technique. To handle this, words are first segmented by morphological dilation and performed connected component labelling. We then employ the Radon transform, discrete wavelet transform, statistical filters and discrete cosine transform to extract the directional multi-resolution spatial features. We tested the features by using linear discriminant analysis, support vector machine and K-nearest neighbour classifiers over 11 different major indian scripts (including Roman) in bi-script and tri-script scenario. In our tests, we have achieved maximum accuracies of 98% and 96% for bi-script and tri-scipt respectively.

关键词： Discrete cosine transforms Discrete wavelet transforms Support vector machines Accuracy Feature extraction Kernel

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Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network

arXiv

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arXiv 2022年

作者： Tomar, Nikhil Kumar Srivastava, Abhishek Bagci, Ulas Jha, Debesh School of Informatics and Computer Science Indira Gandhi National Open University India Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Machine and Hybrid Intelligence Lab Department of Radiology Feinberg School of Medicine Northwestern University United States

The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide. However, the miss rate of colorectal polyp varies significantly among the endoscopists. It is well known that a computer-aided diagnosis (CAD) system can assist endoscopists in detecting colon polyps and minimize the variation among endoscopists. In this study, we introduce a novel deep learning architecture, named MKDCNet, for automatic polyp segmentation robust to significant changes in polyp data distribution. MKDCNet is simply an encoder-decoder neural network that uses the pre-trained ResNet50 as the encoder and novel multiple kernel dilated convolution (MKDC) block that expands the field of view to learn more robust and heterogeneous representation. Extensive experiments on four publicly available polyp datasets and cell nuclei dataset show that the proposed MKDCNet outperforms the state-of-the-art methods when trained and tested on the same dataset as well when tested on unseen polyp datasets from different distributions. With rich results, we demonstrated the robustness of the proposed architecture. From an efficiency perspective, our algorithm can process at (≈ 45) frames per second on RTX 3090 GPU. MKDCNet can be a strong benchmark for building real-time systems for clinical colonoscopies. The code of the proposed MKDCNet is available at https://***/nikhilroxtomar/MKDCNet. © 2022, CC BY.

关键词： Convolution

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CROHME2011: Competition on recognition of Online Handwritten Mathematical Expressions

CROHME2011: Competition on Recognition of Online Handwritten...

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International Conference on Document Analysis and recognition

作者： Harold Mouchere Christian Viard-Gaudin Dae Hwan Kim Jin Hyung Kim Utpal Garain IRCCyN/IVC — UMR CNRS 6597 Ecole Polytechnique de l'Université de Nantes France Division of Computer Science Korea Advanced Institute of Science and Technology Republic of Korea Computer Vision and Pattern Recognition (CVPR) Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781457713507

A competition on recognition of online handwritten mathematical expressions is organized. recognition of mathematical expressions has been an attractive problem for the pattern recognition community because of the presence of enormous uncertainties and ambiguities as encountered during parsing of the two-dimensional structure of expressions. The goal of this competition is to bring out a state of the art for the related research. Three labs come together to organize the event and six other research groups participated the competition. The competition defines a standard format for presenting information, provides a training set of 921 expressions and supplies the underlying grammar for understanding the content of the training data. Participants were invited to submit their recognizers which were tested with a new set of 348 expressions. Systems are evaluated based on four different aspects of the recognition problem. However, the final rating of the systems is done based on their correct expression recognition accuracies. The best expression level recognition accuracy (on the test data) shown by the competing systems is 19.83% whereas a baseline system developed by one of the organizing groups reports an accuracy 22.41% on the same data set.

关键词： Grammar Handwriting recognition Training Accuracy Ink Organizing Communities

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TransDocUNet: A Transformer-based UNet Architecture for Degraded Document Image Binarization 23

TransDocUNet: A Transformer-based UNet Architecture for Degr...

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Proceedings of the Fourteenth indian Conference on computer vision, Graphics and Image Processing

作者： Risab Biswas Soumik Sarkhel Swalpa Kumar Roy Umapada Pal Artificial Intelligence Group Optiks Innovations Pvt. Ltd. (P360) India Department of Computer Science and Engineering Alipurduar Government Engineering and Management College India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9798400716256

The enhancement of historical document images is critical for improving the quality and legibility of scanned or captured document images. Convolutional-based techniques previously generated competitive results for document image binarization, however, due to their inherent locality, these models are often limited in explicitly expressing long-range dependency. Transformers (ViT) have evolved as an alternative design with a global self-attention mechanism to tackle this issue, however, they can result in restricted localization capabilities due to a lack of low-level details. To address this problem, we propose TransDocUNet, a CNN-Transformer hybrid UNet architecture for document image binarization that merits both attention and convolution capabilities in a U-Net architecture and serves as a strong alternative to the existing solutions. The experimental results, obtained using the DIBCO/H-DIBCO datasets, highlight that our proposed method outperforms all the existing competing methods in terms of both objective quality metrics and visual quality assessment, achieving state-of-the-art performance in document image binarization. In addition, we undertake an ablation study to understand the role of dilation in the CNN to capture feature dependencies while reducing the computational cost as well. The findings helped us arrive at the final model and provide valuable insights into the importance of acquiring both global and local contextual information for tasks like enhancing document images.

关键词： Binarization CNN Deep Learning Document Image Enhancement TransUNet UNet vision Transformer

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A New Lightweight Attention-based Model for Emotion recognition on Distorted Social Media Face Images

TechRxiv

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TechRxiv 2023年

作者： Roy, Ayush Shivakumara, Palaiahnakote Pal, Umapada Gornale, Shivanand S. Liu, Cheng-Lin Jadavpur University India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer Science Rani Channamma University Belagavi India Institute of Automation Chinese Academy of Sciences China

The recognition of human emotions remains a challenging task for social media images. This is due to distortions created by different social media conflict with the minute changes in facial expression. This study presents a new model called the Global Spectral-Spatial Attention Network (GSSAN), which leverages both local and global information simultaneously. The proposed model comprises a shallow Convolutional Neural Network (CNN) with an MBResNext block, which integrates the features extracted from MobileNet, ResNet, and DenseNet for extracting local features. In addition, to strengthen the discriminating power of the features, GSSAN incorporates Fourier features, which provide essential cues for minute changes in the face images. To test the proposed model for emotion recognition using social media images, we conduct experiments on two widely-used datasets: FER-2013 and AffectNet. The same benchmark datasets are uploaded and downloaded to create a distorted social media image dataset to test the proposed model. Experiments on distorted social media images dataset show that the model surpasses the accuracy of SOTA models by 0.69% for FER-2013 and 0.51% for AffectNet social mediad datasets. The same inference can be drawn from the experiments on standard datasets. © 2023, CC BY.

关键词： Face recognition

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DELP-DAR system for license plate detection and recognition

arXiv

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arXiv 2019年

作者： Selmi, Zied Halima, Mohamed Ben Pal, Umapada Alimi, M. Adel REGIM-Lab: Research Groups in Intelligent Machines University of Sfax ENIS BP 1173 Sfax3038 Tunisia Computer vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road olkata700108 India

Automatic License Plate detection and recognition (ALPR) is a quite popular and active research topic in the field of computer vision, image processing and intelligent transport systems. ALPR is used to make detection and recognition processes more robust and efficient in highly complicated environments and backgrounds. Several research investigations are still necessary due to some constraints such as: completeness of numbering systems of countries, different colors, various languages, multiple sizes and varied fonts. For this, we present in this paper an automatic framework for License Plate (LP) detection and recognition from complex scenes. Our framework is based on mask region convolutional neural networks used for LP detection, segmentation and recognition. Although some studies have focused on LP detection, LP recognition, LP segmentation or just two of them, our study uses the maskr-cnn in the three stages. The evaluation of our framework is enhanced by four datasets for different countries and consequently with various languages. In fact, it tested on four datasets including images captured from multiple scenes under numerous conditions such as varied orientation, poor quality images, blurred images and complex environmental backgrounds. Extensive experiments show the robustness and efficiency of our suggested framework in all datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： License plates (automobile)

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A colour image encryption based on DNA coding and chaotic sequences

A colour image encryption based on DNA coding and chaotic se...

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International Conference on Emerging Trends and Applications in computer Science (ICETACS)

作者： Sukalyan Som Atanu Kotal Ayantika Chatterjee Soumista Dey Sarbani Palit Department of Computer Science Barrackpore Rastraguru Surendranath College Kolkata West Bengal India Department of Computer Science and Engineering Techno India College of Technology Kolkata West Bengal India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata West Bengal India

In this communication, a Chaos Based Symmetric Key Encryption of RGB Color Images with DNA Coding and a Chaos based Pseudorandom Binary Number Generator (PRBNG) has been proposed. In the proposed algorithm, the plain image is first scrambled using generalized Arnold Cat Map to achieve confusion. The scrambled image pixel are converted to DNA codes and again reconverted to integers where the choice of DNA coding rule is made pseudorandom based on the binary sequences generated by chaos based pseudorandom binary number generator. The integers thus obtained are diffused by performing exclusive OR operation with the integer sequences generated by 1D Logistic map producing the cipher image. The experimental results depicts that the proposed algorithm can successfully encrypt and decrypt RGB color images with secret keys. The simulation analysis also exhibit that the proposed method is loss-less, secure and efficient measured in terms of statistical tests(like histogram analysis, correlation coefficient analysis, measures of central tendency and dispersion), key sensitivity test, key space analysis, information entropy test, encryption quality by MSE, PSNR, NPCR and UACI.

关键词： DNA Ciphers Encryption Chaotic communication Logistics

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Multiple Training - One Test Methodology for Handwritten Word-Script Identification

Multiple Training - One Test Methodology for Handwritten Wor...

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International Workshop on Frontiers in Handwriting recognition

作者： Miguel A. Ferrer Aythami Morales Nayara Rodríguez Umapada Pal Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones Universidad de Las Palmas de Gran Canaria Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Script identification is an important area in handwriting document image analysis field. The script identification at word level on documents written in multiple scripts is an open challenge for the scientific community and a real concern in countries with multiple official languages, e. G. The country like India. Such documents usually contain two scripts: the most of the document are written in the regional script while some words, acronyms or numbers are written in Roman script. In this case a word or even a character level script identification is required to locate the second script characters in the document. Here the major problem is the few script descriptors available for the script estimation which convey high error rates. The literatures try to address this problem by looking for more efficient descriptors. In this paper we propose a Multiple Training - One Test technique to alleviate this problem. Several classifiers are trained, each one with words of similar amount of information. A scale invariable word information index is defined for this sake. To identify the script of a query word, its word information index is worked out, and its script is identified with the most appropriate classifier. Accuracy improvements has been obtained with this promising technique, especially for the shorten words.

关键词： Training Histograms Feature extraction Indexes Accuracy Testing

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ICDAR 2023 Video Text Reading Competition for Dense and Small Text

arXiv

引用

arXiv 2023年

作者： Wu, Weijia Zhao, Yuzhong Li, Zhuang Li, Jiahong Shou, Mike Zheng Pal, Umapada Karatzas, Dimosthenis Bai, Xiang Zhejiang University China University of Chinese Academy of Sciences China Kuaishou Technology China National University of Singapore Singapore Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Computer Vision Centre Universitat Autónoma de Barcelona Spain Huazhong University of Science and Technology China

Recently, video text detection, tracking and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenario, while ignore extreme video texts challenges, i.e., dense and small text in various scenarios. In this competition report, we establish a video text reading benchmark, named DSText, which focuses on dense and small text reading challenge in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, new challenge for video text spotter. 2) High-proportioned small texts. 3) Various new scenarios, e.g., ‘Game’, ‘Sports’, etc. The proposed DSText includes 100 video clips from 12 open scenarios, supporting two tasks (i.e., video text tracking (Task 1) and end-to-end video text spotting (Task2)). During the competition period (opened on 15th February, 2023 and closed on 20th March, 2023), a total of 24 teams participated in the three proposed tasks with around 30 valid submissions, respectively. In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols and the results summaries of the ICDAR 2023 on DSText competition. Moreover, we hope the benchmark will promise the video text research in the community. © 2023, CC BY.

关键词： Character recognition

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New Tampered Features for Scene and Caption Text Classification in Video Frame

New Tampered Features for Scene and Caption Text Classificat...

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International Workshop on Frontiers in Handwriting recognition

作者： Sangheeta Roy Palaiahnakote Shivakumara Umapada Pal Tong Lu Chew Lim Tan Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Computer Science National University of Singapore

ISBN: (纸本)9781509009824

The presence of both caption/graphics/superimposed and scene texts in video frames is the major cause for the poor accuracy of text recognition methods. This paper proposes an approach for identifying tampered information by analyzing the spatial distribution of DCT coefficients in a new way for classifying caption and scene text. Since caption text is edited/superimposed, which results in artificially created texts comparing to scene texts that exist naturally in frames. We exploit this fact to identify the presence of caption and scene texts in video frames based on the advantage of DCT coefficients. The proposed method analyzes the distributions of both zero and non-zero coefficients (only positive values) locally by moving a window, and studies histogram operations over each input text line image. This generates line graphs for respective zero and non-zero coefficient coordinates. We further study the behavior of text lines, namely, linearity and smoothness based on centroid location analysis, and the principal axis direction of each text line for classification. Experimental results on standard datasets, namely, ICDAR 2013 video, 2015 video, YVT video and our own data, show that the performances of text recognition methods are improved significantly after-classification compared to before-classification.

关键词： Discrete cosine transforms Text recognition Image color analysis Linearity Bars Character recognition Mathematical model

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