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6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014

作者： Chakraborty, Anirban Ghosh, Kripabandhu Roy, Utpal Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata700108 India Department of Computer and System Sciences Siksha-Bhavana Visva-Bharati Santiniketan731235 India

ISBN: (纸本)9789897580482

OCR errors hurt retrieval performance to a great extent. Research has been done on modelling and correction of OCR errors. However, most of the existing systems use language dependent resources or training texts for studying the nature of errors. Not much research has been reported on improving retrieval performance from erroneous text when no training data is available. We propose an algorithm of detecting OCR errors and improving retrieval performance from the erroneous corpus. We present two versions of the algorithm: one based on word cooccurrence and the other based on Pointwise Mutual Information. Our algorithm does not use any training data or any language specific resources like thesaurus. It also does not use any knowledge about the language except that the word delimiter is a blank space. We have tested our algorithm on erroneous Bangla FIRE collection and obtained significant improvements. Copyright © 2014 SCITEPRESS - Science and Technology Publications All rights reserved.

关键词： Errors

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A Global-to-Local Approach to Binarization of Degraded Document Images

A Global-to-Local Approach to Binarization of Degraded Docum...

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International Conference on pattern recognition

作者： Barun Biswas Ujjwal Bhattacharya Bidyut B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata 108 India

ISBN: (纸本)9781479952106

This article deals with binarization of degraded document images. In the proposed approach, Canny edge image of the input degraded document image is obtained after blurring it with a Gaussian filter. Next, the gray values of the two pixels of the input image at the left and right of each edge pixel are noted to form a histogram of these gray values which possesses two distinct peaks and the lowest valley between them provides the global threshold value. Each pixel with gray value greater than the above threshold is turned as background pixel. A small square window is considered around each non-background pixel and certain simple statistics are computed on the gray values of the pixels of this small window based on which the said pixel is turned either background or foreground. Such a local thresholding method at the latter stage can efficiently handle various degradations in the document. The binarized image so obtained is finally subjected to certain common post-processing operations. The proposed method has been compared with a few existing binarization techniques.

关键词： Image edge detection Histograms Degradation Smoothing methods PSNR Image color analysis

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A New Transformer-Based Approach for Text Detection in Shaky and Non-shaky Day-Night Video 1

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7th Asian Conference on pattern recognition, ACPR 2023

作者： Halder, Arnab Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Nanjing University Nanjing China University of Technology Sydney Sydney Australia

ISBN: (数字)9783031476372

ISBN: (纸本)9783031476365

Text detection in shaky and non-shaky videos is challenging because of variations caused by day and night videos. In addition, moving objects, vehicles, and humans in the video make the text detection problems more challenging in contrast to text detection in normal natural scene images. Motivated by the capacity of the transformer, we propose a new transformer-based approach for detecting text in both shaky and non-shaky day-night videos. To reduce the effect of object movement, poor quality, and other challenges mentioned above, the proposed work explores temporal frames for obtaining activation frames based on similarity and dissimilarity measures. For estimating similarity and dissimilarity, our method extracts luminance, contrast, and structural features. The activation frames are fed to the transformer which comprises an encoder, decoder, and feed-forward network for text detection in shaky and non-shaky day-night video. Since it is the first work, we create our own dataset for experimentation. To show the effectiveness of the proposed method, experiments are conducted on a standard dataset called the ICDAR-2015 video dataset. The results on our dataset and standard dataset show that the proposed model is superior to state-of-the-art methods in terms of recall, precision, and F-measure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical activation

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A new method for sclera vessel recognition using OLBP

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2012 International Conference on Service-Oriented Computing, ICSOC 2012

作者： Das, Abhijit Pal, Umapada Ferrer Ballester, Miguel A. Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9783319029603

This paper proposes a new sclera vessel recognition technique. The vessel patterns of sclera are unique for each individual and this can be utilized to identify a person uniquely. In this research we have used a time adaptive active contour-based region growing technique for sclera segmentation. Prior to that, we have made some tonal and illumination correction to get a clearer sclera area without the distributing vessel structure. This is because the presence of complex vessel structures occasionally affects the region-growing process. The sclera vessels are not prominent in the images, so in order to make them clearly visible, a local image enhancement process using a Haar high pass filter is incorporated. To get the total orientation of the vessels, we have used Orientated Local Binary pattern (OLBP). The OLBP images of each class are used for template matching for classification by calculating the minimum Hamming Distance. We have used the UBIRIS version 1 dataset for the experimentation of our research. The proposed approach has achieved high recognition accuracy employing the above-mentioned dataset. © Springer International Publishing 2013.

关键词： Local binary pattern

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Convex hull based approach for multi-oriented character recognition from graphical documents

Convex hull based approach for multi-oriented character reco...

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作者： Roy, Partha Pratim Pal, Umapada Lladós, Josep Kimura, Fumitaka Computer Vision Center Universitat Autònoma De Barcelona 08193 Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata - 108 India Graduate School of Engineering Mie University 1577 Kurimamachiya Mie 514-8504 Japan

ISBN: (纸本)9781424421756

In this paper, we present a scheme towards recognition of English character in multi-scale and multi-oriented environments. Graphical document such as map consists of text lines which appear in different orientation. Sometimes, characters in a single word may follow a curvi-linear way to annotate the graphical curve lines. For recognition of such multi-scale and multi-oriented characters a Support Vector Machine (SVM) based scheme is presented in this paper. The feature used here is invariant to character orientation. Circular ring and convex hull have been used along with angular information of the contour pixels of the character to make the feature rotation invariant. We tested our proposed scheme on two different datasets. Combining circular and convex hull feature we have obtained 96.73% and 99.56% accuracy in these two datasets. © 2008 IEEE.

关键词： Support vector machines

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Graphically Residual Attentive Network for tackling aerial image occlusion

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computers and Electrical Engineering 2025年 125卷

作者： Praveen Kumar Pradhan Kunal Purkayastha Aaditya Lochan Sharma Udayan Baruah Biswaraj Sen Palash Ghosal Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Majitar Rangpo 737136 Sikkim India Centre for Computers and Communication Technology Chisopani South Sikkim 737136 Sikkim India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata 700108 West Bengal India Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Majitar Rangpo 737136 Sikkim India Birangana Sati Sadhani Rajyik Vishwavidyalaya Golaghat 785621 Assam India

Deep learning has rapidly advanced, enabling new applications such as object detection, text recognition, and occlusion handling. However, challenges remain in the detection of objects in complex environments such as aerial images where things like motion blur, low light, and significant occlusion occur. This paper addresses a similar challenge by introducing a novel supervised framework, the Graphically Residual Attentive Network (GRESIDAN). In the same model, GRESIDAN integrates three synergistic pipelines for object detection, occlusion detection, and occlusion removal. GRESIDAN uses a residually attentive block combining ResNet-18 and a multi-headed attention mechanism to improve feature extraction and detection accuracy in low-quality, occluded aerial images. A graphically attentive occlusion detection pipeline is implemented to handle occlusion, segment better, and mask out the occluder in the aerial image. The GRESIDAN model is validated on the COCO-2017 dataset and a custom private aerial object detection dataset, outperforming the state-of-the-art methods in handling occlusion and detecting objects. Our contributions provide a robust solution to the problem of detecting and handling occluded objects in aerial imagery, pushing the boundaries of automated visual recognition in challenging real-world scenarios. The code for public use in training and testing is available on GitHub .

关键词： Image segmentation

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CNN based common approach to handwritten character recognition of multiple scripts

CNN based common approach to handwritten character recogniti...

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International Conference on Document Analysis and recognition

作者： Durjoy Sen Maitra Ujjwal Bhattacharya Swapan K. Parui Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India

ISBN: (纸本)9781479918065

There are many scripts in the world, several of which are used by hundreds of millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. However, convolutional neural network (CNN) has recently been used as an efficient unsupervised feature vector extractor. Although such a network can be used as a unified framework for both feature extraction and classification, it is more efficient as a feature extractor than as a classifier. In the present study, we performed certain amount of training of a 5-layer CNN for a moderately large class character recognition problem. We used this CNN trained for a larger class recognition problem towards feature extraction of samples of several smaller class recognition problems. In each case, a distinct Support Vector Machine (SVM) was used as the corresponding classifier. In particular, the CNN of the present study is trained using samples of a standard 50-class Bangla basic character database and features have been extracted for 5 different 10-class numeral recognition problems of English, Devanagari, Bangla, Telugu and Oriya each of which is an official indian script. recognition accuracies are comparable with the state-of-the-art.

关键词： Handwriting recognition Databases Accuracy Training

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A CNN Based Framework for Unistroke Numeral recognition in Air-Writing

A CNN Based Framework for Unistroke Numeral Recognition in A...

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International Workshop on Frontiers in Handwriting recognition

作者： Prasun Roy Subhankar Ghosh Umapada Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata WB India

Air-writing refers to virtually writing linguistic characters through hand gestures in three dimensional space with six degrees of freedom. In this paper a generic video camera dependent convolutional neural network (CNN) based air-writing framework has been proposed. Gestures are performed using a marker of fixed color in front of a generic video camera followed by color based segmentation to identify the marker and track the trajectory of marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies greatly on the illumination condition due to color based segmentation. In a less fluctuating illumination condition the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework achieved 97.7%, 95.4% and 93.7% recognition rate in person independent evaluation over English, Bengali and Devanagari numerals, respectively.

关键词： Training Testing Sensors Cameras Writing Image color analysis Convolution

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Fuzzy logic based selera recognition

Fuzzy logic based selera recognition

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2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014

作者： Das, Abhijit Pal, Umapada Ballester, Miguel Angel Ferrer Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9781479920723

In this paper a selera recognition and validation system is proposed. Here selera segmentation was performed by Fuzzy logic-based clustering. Since the selera vessels are not prominent, image enhancement was required. A Fuzzy logic-based Brightness Preserving Dynamic Fuzzy Histogram Equalization and discrete Meyer wavelet was used to enhance the vessel patterns. For feature extraction, the Dense Local Binary pattern (D-LBP) was used. D-LBP patch descriptors of each training image are used to form a bag of features, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. An encouraging Equal Error Rate (EER) of 4.31% was achieved in our experiments. © 2014 IEEE.

关键词： Support vector machines

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Sclera vessel pattern synthesis based on a non-parametric texture synthesis technique

Sclera vessel pattern synthesis based on a non-parametric te...

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International Conference on computer vision and Image Processing, CVIP 2016

作者： Das, Abhijit Mondal, Prabir Pal, Umapada Blumenstein, Michael Ferrer, Miguel A. Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9789811021060

This work proposes a sclera vessel texture pattern synthesis technique. Sclera texture was synthesized by a non-parametric based texture regeneration technique. A small number of classes from the UBIRIS version: 1 dataset was employed as primitive images. An appreciable result was achieved which solicits the successful synthesis of sclera texture patterns. It is difficult to get a huge collection real sclera data and hence such synthetic data will be useful to the researchers. © Springer Science+Business Media Singapore 2017.

关键词： Biometrics pattern Sclera Synthesis Texture

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