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arXiv 2022年

作者： Roy, Prasun Bhattacharya, Saumik Ghosh, Subhankar Pal, Umapada The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Indian Institute of Technology Kharagpur India

Pose transfer refers to the probabilistic image generation of a person with a previously unseen novel pose from another image of that person having a different pose. Due to potential academic and commercial applications, this problem is extensively studied in recent years. Among the various approaches to the problem, attention guided progressive generation is shown to produce state-of-the-art results in most cases. In this paper, we present an improved network architecture for pose transfer by introducing attention links at every resolution level of the encoder and decoder. By utilizing such dense multi-scale attention guided approach, we are able to achieve significant improvement over the existing methods both visually and analytically. We conclude our findings with extensive qualitative and quantitative comparisons against several existing methods on the DeepFashion dataset. © 2022, CC BY.

关键词： Network architecture

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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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A New Method for Detecting Altered Text in Document Images 2nd

A New Method for Detecting Altered Text in Document Images

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2nd International Conference on pattern recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Lopresti, Daniel Seraogi, Bhagesh Chaudhuri, Bidyut B. 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 Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9783030598297

As more and more office documents are captured, stored, and shared in digital format, and as image editing software becomes increasingly more powerful, there is a growing concern about document authenticity. For example, texts in property documents can be altered to make an illegal deal, or the date on an airline ticket can be altered to gain entry to airport terminals by breaching security. To prevent such illicit activities, this paper presents a new method for detecting altered text in a document. The proposed method explores the relationship between positive and negative coefficients of a DCT to extract the effect of distortions caused by tampering operations. Here we divide DCT coefficients into positive and negative classes, then reconstructs images from the inverse DCT of the respective positive and negative coefficients. Next, we perform Laplacian filtering over reconstructed images for widening the gap between the values of text and other pixels. Then filtered images of positive and negative coefficients are fused by an average operation. For a fused image, we generate Canny and Sobel edge images in order to investigate the effect of distortion through quality measures, namely, MSE, PSNR and SSIM used as features. In addition, for the fused image, the proposed method extracts features based on histograms over the residual images. The features are then passed on to a deep Convolutional Neural Network for classification. The proposed method is tested on our own dataset as well as two standard datasets, namely IMEI and the ICPR 2018 Fraud Contest dataset. The results show that the proposed method is effective and outperforms existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Inverse problems

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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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Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes

arXiv

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arXiv 2023年

作者： Das, Alloy Biswas, Sanket Pal, Umapada Lladós, Josep Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India The Computer Vision Center Computer Science Department Universitat Autónoma de Barcelona Barcelona Spain

When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models will be released upon acceptance. © 2023, CC BY-SA.

关键词： Benchmarking

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Document-Zone Classification in Torn Documents

Document-Zone Classification in Torn Documents

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International Workshop on Frontiers in Handwriting recognition

作者： Sukalpa Chanda Katrin Franke Umapada Pal Department of Computer Science and Media Technology Gjovik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

Arbitrary orientation and sparse data content are common characteristics of torn document. To ensure accuracy and reliability in computer-based analysis, content-zone segmentation is required. In our previous work, we studied segmentation of handwritten and printed text. A questioned document-piece in the form of an office note, however, might also contain non-text data like logos, graphics, and pictures. Hence a more precise content-zone classification is required. In this paper we propose a two-tier approach for non-text, handwriting and printed text segmentation. The first tier aims to discriminate text and non-text regions. The second tier classifies handwritten and printed text within all text zones identified during the first tier. Gabor features and chain-code features are used in Tier-1 and Tier-2, respectively. By using SVM classifier we successfully identified 97.65% of 31,227 text regions in our current test data. The proposed approach identified 98.69% of printed and 96.39% of handwritten text amongst all identified text regions.

关键词： Accuracy Graphics Gabor filters Support vector machines Pixel Feature extraction Image segmentation

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Text Independent Writer Identification for Oriya Script

Text Independent Writer Identification for Oriya Script

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IAPR International Workshop on Document Analysis Systems, DAS

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 ensured. Lack of data threatens system reliability in such cases. We here propose a writer identification system for Oriya script which is capable of performing reasonably well even with small amount of text. Experiments with curvature feature are reported here, using Support Vector Machine (SVM) as classifier. We got promising results of 94.00% writer identification accuracy at first top choice and 99% when considering first three top choices.

关键词： Accuracy Support vector machines Shape Training Handwriting recognition Kernel

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Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes

Diving into the Depths of Spotting Text in Multi-Domain Nois...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alloy Das Sanket Biswas Umapada Pal Josep Lladós Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science Department Computer Vision Center Universitat Autónoma de Barcelona Barcelona Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

关键词： Training Codes Accuracy Superresolution Benchmark testing Transformers Data models

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A direct approach for word and character segmentation in run-length compressed documents with an application to word spotting

A direct approach for word and character segmentation in run...

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International Conference on Document Analysis and recognition

作者： Mohammed Javed P. Nagabhushan B.B. Chaudhuri Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Segmentation of a text document into lines, words and characters is an important objective in application like OCR and related analytics. However in today's scenario, the documents are compressed for archival and transmission efficiency. Text segmentation in compressed documents warrants decompression, and needs additional computing resources. In this backdrop, the paper proposes a method for text segmentation directly in run-length compressed, printed English text documents. Line segmentation is done using the projection profile technique. Further segmentation into words and characters is accomplished by tracing the white runs along the base region of the text line. During the process, a run based region growing technique is applied in the spatial neighborhood of the white runs to trace the vertical space between the characters. After detecting the character spaces in the entire text line, the decision of word space and character space is made by computing the average character space. Subsequently based on the spatial position of the detected words and characters, their respective compressed segments are extracted. The proposed algorithm is tested with 1083 compressed text lines, and F-measure of 97.93% and 92.86% respectively for word and character segmentation are obtained. Finally an application of word spotting is also presented.

关键词： Optical character recognition software Image coding Adaptation models

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Automatic extraction of correlation-entropy features for text document analysis directly in run-length compressed domain

Automatic extraction of correlation-entropy features for tex...

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International Conference on Document Analysis and recognition

Automatic feature extraction plays a pivotal role in defining the overall performance of any Document Image Analysis system, which conventionally operates directly over uncompressed images, although most of the real time systems such as fax machines, digital libraries and e-governance applications accrue and archive the documents in the compressed form for the sake of storage and transfer efficiencies. However, this infers that the compressed documents need to be decompressed before carrying out any operation or analysis which warrants additional computing resources. This limitation in existing systems instigates motivation to explore for feature extraction techniques directly from the compressed documents and eventually design a document analysis system that works directly in compressed domain. Therefore, this research work proposes to extract novel correlation-entropy features directly from run-length compressed TIFF documents. Further, the research work also investigates different methods to demonstrate some of the straight forward application of the proposed features in carrying out compressed document image analysis such as text and non-text component detection, and subsequently performing compressed text line segmentation and characterization, all carried out in the compressed version of the printed text document without going through the stage of decompression. Finally, the experimental results reported validate the developed algorithms and also illustrate that the proposed features are quite powerful in distinguishing compressed text and non-text components.

关键词： Image coding Image segmentation

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