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arXiv 2022年

作者： Manna, Siladittya Dey, Rakesh Chakraborty, Souvik Computer Vision and Pattern Recognition Unit Indian Statistical Institue Kolkata India Computer Vision Researcher

Applications on Medical Image Analysis suffer from acute shortage of large volume of data properly annotated by medical experts. Supervised Learning algorithms require a large volumes of balanced data to learn robust representations. Often supervised learning algorithms require various techniques to deal with imbalanced data. Self-supervised learning algorithms on the other hand are robust to imbalance in the data and are capable of learning robust representations. In this work, we train a 3D BYOL self-supervised model using gradient accumulation technique to deal with the large number of samples in a batch generally required in a self-supervised algorithm. To the best of our knowledge, this work is one of the first of its kind in this domain. We compare the results obtained through our experiments in the downstream task of ACL Tear Injury detection with the contemporary self-supervised pre-training methods and also with ResNet3D-18 initialized with the Kinetics-400 pre-trained weights. From the downstream task experiments, it is evident that the proposed framework outperforms the existing baselines. © 2022, CC0.

关键词： Supervised learning

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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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Anaphora Resolution in Bangla Using Global Discourse Knowledge

Anaphora Resolution in Bangla Using Global Discourse Knowled...

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International Conference on Asian Language Processing (IALP)

作者： Apurbalal Senapati Utpal Garain Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper presents a pronominal anaphora resolution (PAR) approach that makes use of the global discourse knowledge along with other traditional features. So far the features used in finding the referent of an anaphoric pronoun are computed locally. Normally the sentence containing the anaphor and a few sentences immediately before form the local context. In this process, the knowledge base gets updated as more and more of the discourse is processed. Keeping this approach as the core, the present paper explores use of some prior knowledge after examining the entire discourse (whole article). Addition of this processing step improves the PAR's efficiency. This improvement is demonstrated using ICON 2011 Bangla dataset.

关键词： Pragmatics Natural language processing Organizations Communities computer vision pattern recognition Roads

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Extraction of type style based meta-information from imaged documents

Extraction of type style based meta-information from imaged ...

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International Conference on Document Analysis and recognition

作者： U. Garain B.B. Chaudhuri Computer Vision & Pattern Recognition Unit Indian Statistical Institute Calcutta India

Extraction of some meta-information from printed documents without an OCR approach is considered. It can be statistically verified that important terms in articles are printed in italic, bold and all capital style. Detection of these type styles helps in automatic extraction of the lines containing titles, authors' names, subtitles, references as well as sentences having important terms occurring in the text. It also helps in improving the OCR performance for reading the italic text. Some experimental results on the performance of the approach on good quality as well as degraded document images are presented.

关键词： Optical character recognition software Data mining Sections computer vision pattern recognition Postal services Read only memory Pressing Search engines Image converters

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Multi-script line identification from Indian documents

Multi-script line identification from Indian documents

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International Conference on Document Analysis and recognition

作者： U. Pal S. Sinha B.B. Chaudhuri Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)0769519601

A document page may contain two or more different scripts. For Optical Character recognition (OCR) of such a document page, it is necessary to separate different scripts before feeding them to their individual OCR system. In this paper an automatic scheme is presented to identify text lines of different Indian scripts from a document. For the separation task at first the scripts are grouped into a few classes according to script characteristics. Next feature based on water reservoir principle, contour tracing, profile etc. are employed to identify them without any expensive OCR-like algorithms. At present, the system has an overall accuracy of about 97.52%.

关键词： Natural languages Optical character recognition software computer vision pattern recognition Character recognition Water resources Reservoirs Constitution Writing Gabor filters

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F-ratio based weighted feature extraction for similar shape character recognition

F-ratio based weighted feature extraction for similar shape ...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Wakabayashi, T. Pal, U. Kimura, F. Miyake, Y. Graduate School of Engineering Mie University TSU 1577 Kurimamachiya-cho Mie 514-8507 Japan Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India

ISBN: (纸本)9780769537252

recognition of handwritten similar shaped character is a difficult problem and in character recognition system most of the errors occur from similar shaped characters. In this paper we proposed a novel feature extraction technique to improve the recognition results of two similar shaped characters. The technique is based on F-ratio (Fisher Ratio), a statistical measure defined by the ratio to the between-class variance and within-class variance. F-ratio modifies the feature vector of two similar shape characters by weighting the feature elements. This weighting scheme enhances the feature elements that belongs to the distinguishable portions of the similar shaped characters and reduces the feature elements of the common portion of the characters, so that similar shaped characters can be identified easily. We considered pair of handwritten similar shape characters of different scripts like Arabic/Persian, Devnagari English, Bangla, Oriya, Tamil, Kannada, Telugu etc. and we noted that f-ratio based feature weighting shows better recognition results. © 2009 IEEE.

关键词： Character recognition

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Automatic detection of italic, bold and all-capital words in document images

Automatic detection of italic, bold and all-capital words in...

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International Conference on pattern recognition

作者： B.B. Chaudhuri U. Garain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India

We propose simple and fast algorithms for detection of italic, bold and all-capital words without doing actual character recognition. We present a statistical study which reveals that the detection of such words may play a key role in automatic information retrieval from documents. Moreover, detection of italic words can be used to improve the recognition accuracy of a text recognition system. Considerable number of document images have been tested and our algorithms give accurate results on all the tested images, and the algorithms are very easy to implement.

关键词： Optical character recognition software Information retrieval Character recognition Text recognition Testing Books computer vision pattern recognition Software systems Degradation

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recognition of printed Urdu script

Recognition of printed Urdu script

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International Conference on Document Analysis and recognition

作者： U. Pal A. Sarkar Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)0769519601

This paper deals with an Optical Character recognition system for printed Urdu, a popular Indian script. The development of OCR for this script is difficult because (i) a large number of characters have to be recognized (ii) there are many similar shaped characters. In the proposed system individual characters are recognized using a combination of topological, contour and water reservoir concept based features. The feature detection methods are simple and robust. A prototype of the system has been tested on printed Urdu characters and currently achieves 97.8% character level accuracy on average.

关键词： Reservoirs Water resources Optical character recognition software Character recognition Shape computer vision Image segmentation Water storage pattern recognition Robustness

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Can an Image Tell the Tale: Looking Beyond the Haze to Determine PM2.5 Concentration

Can an Image Tell the Tale: Looking Beyond the Haze to Deter...

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International Joint Conference on Neural Networks (IJCNN)

作者： Sagarnil Chakraborty Sarbani Palit Harsh Bhandari Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the past few decades, due to rapid growth in industrialization, there has been a steady decline of the air quality along with an increase in the concentration of PM2.5. It is well known that a high PM2.5 concentration adversely affects the environment and has hazardous impact on public health. Therefore, it is important to monitor the PM2.5 concentration at geographic locations where air quality monitoring stations are presently unavailable, especially in remote areas. Unfortunately, installation of such monitoring stations requires expensive instruments and constant maintenance. This paper presents a novel, low-cost and portable alternative to such measurement apparatus, where PM2.5 concentration is estimated based on image input obtained from a camera. The novelty of the present work lies in its hitherto unique attempt to capture information regarding PM2.5 content from visibility degradation caused by the pollutant which is further supplemented by important knowledge regarding seasonal and diurnal variation of it. The latter has a crucial role in the prevention of confounding effects arising from the presence of other weather and atmospheric elements. Another important highlight is the use of a full reference image metric as a feature, for which a powerful, dehazing algorithm has been employed. The results obtained are extremely promising, providing a close to accurate estimation of PM2.5 concentration with R 2 values far higher than reported in the literature. To summarize, the construction of a unique feature set, together with an appropriate machine learning algorithm, lead to an extremely reliable, stand-alone approach, deployable on a hand-held device such as a mobile and is a very significant contribution indeed of the proposed approach.

关键词： Machine learning algorithms Prevention and mitigation Instruments Neural networks Air quality Pollution measurement Maintenance

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Combination of Features for Efficient recognition of Offline Handwritten Devanagari Words

Combination of Features for Efficient Recognition of Offline...

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International Workshop on Frontiers in Handwriting recognition

作者： Bikash Shaw Ujjwal Bhattacharya Swapan K. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this article, we describe our recent study of a novel combination of two feature vectors for holistic recognition of offline handwritten word images. In the literature, both contour and skeleton based feature representations have been studied for offline handwriting recognition purpose. However, to the best of our knowledge, there is no such study in which combination of the two feature representations have been considered for the purpose. In the proposed recognition scheme, we use multiclass SVM as the classifier. We have implemented the proposed approach for holistic recognition of Devanagari handwritten town names and tested its performance on a large handwritten word sample database of 100 Indian town names written in Devanagari. Experimental results show sharp improvement in recognition accuracy over the use of any of the individual feature representation schemes. The proposed approach is script independent and can be used for development of a holistic handwritten word image recognition of any script.

关键词： Feature extraction Handwriting recognition Histograms Vectors Shape Support vector machines Skeleton

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