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5th Asian Conference on pattern recognition, ACPR 2019

作者： Grouver, Anaica Shivakumara, Palaiahnakote Kaljahi, Maryam Asadzadeh Chetty, Bhaarat Pal, Umapada Lu, Tong Hemantha Kumar, G. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Google Developers Group NASDAQ Bangalore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Mysore MysoreKarnataka India

ISBN: (纸本)9783030412982

Due to change in mindset and living style of humans, the numbers of diversified marriages are increasing all around the world irrespective of race, color, religion and culture. As a result, it is challenging for research community to identify multi type family photos, namely, normal family (family of the same race, religion or culture), multi-culture family (family of different culture, religion or race) from the family and non-family photos (images with friends, colleagues, etc.). In this work, we present a new method that combines spatial density information with phase angle for multi-type family photo classification. The proposed method uses three facial key points, namely, left-eye, right-eye and nose, for the features which are based on color, roughness and wrinkleless of faces, these are prominent for extracting unique cues for classification. The correlations between features of Left & Right Eyes, Left Eye & Nose and Right Eye & Nose are computed for all the faces in an image. This results in feature vectors for respective spatial density and phase angle information. Furthermore, the proposed method fuses the feature vectors and feeds them to the Convolutional Neural Network (CNN) for the classification of the above-three class problem. Experiments conducted on our database which contains three classes, namely, multi-cultural, normal and non-family images and the benchmark databases (due to Maryam et al. and Wang et al.) which contain two class-family and non-family images, show that the proposed method outperforms the existing methods in terms of classification rate for all the three databases. © 2020, Springer Nature Switzerland AG.

关键词： Database systems

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An Approach of Strike-Through Text Identification from Handwritten Documents

An Approach of Strike-Through Text Identification from Handw...

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International Workshop on Frontiers in Handwriting recognition

作者： Chandranath Adak Bidyut B. Chaudhuri Dept. of Computer Science & Engg University of Kalyani West Bengal India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

A handwritten document may contain strike-through texts. If such texts are fed into an OCR system, the output will be garbage. In this paper, we propose a scheme to detect such strike-through texts/words. Using a graph based model, we represent a textual connected component as a graph. The start/end and intersection points of the ink-strokes of a component are marked as graph nodes. There exists an edge between two nodes if they are connected by object (ink) pixels. By eliminating parallel edges and self loops we obtain a simple, undirected, edge-weighted graph of the text-component. The edge-weight is found by adding horizontal/vertical moves weighted by 1 and diagonal moves weighted by √2. In this graph, we find the shortest path which is nearly as long as the width of the text component and maintains a reasonable degree of straightness. This path, if exist, is identified as the strike-through line. Here we deal with handwritten documents in English, Bengali and Devanagari script. Our approach delivers fairly good results.

关键词： Handwriting recognition Hidden Markov models Character recognition Optical character recognition software Image edge detection Ink Databases

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A Two Stage Approach for Handwritten Malayalam Character recognition

A Two Stage Approach for Handwritten Malayalam Character Rec...

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International Workshop on Frontiers in Handwriting recognition

作者： Jomy John K.V. Pramod Kannan Balakrishnan Bidyut B. Chaudhuri Dept. of Computer Applications Cochin University of Science and Technology Kochi India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

Handwritten character recognition is still a research challenge in OCR discipline, especially for Indian scripts. This paper deals with handwritten Malayalam, a major Indian script, where past work considered a small subset of characters only. In this paper we deal with complete set of basic characters, vowel and consonant signs and compound characters that may be present in the script. Here the recognition task is more difficult because of large numbers of classes and high interclass similarity. To tackle the problem, we proposed a two-stage approach. The first stage is a group classifier, where a group consists of similar characters and those that misclassify among themselves. In the second stage, a character assigned to a group in the first stage is classified to a particular character class. The proposed scheme is more accurate and efficient than a single stage scheme.

关键词： Compounds Character recognition Support vector machines Shape Handwriting recognition Feature extraction Databases

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ICDAR2015 Competition on Video Script Identification (CVSI 2015)

ICDAR2015 Competition on Video Script Identification (CVSI 2...

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International Conference on Document Analysis and recognition

作者： Nabin Sharma Ranju Mandal Rabi Sharma Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479918065

This paper presents the final results of the ICDAR 2015 Competition on Video Script Identification. A description and performance of the participating systems in the competition are reported. The general objective of the competition is to evaluate and benchmark the available methods on word-wise video script identification. It also provides a platform for researchers around the globe to particularly address the video script identification problem and video text recognition in general. The competition was organised around four different tasks involving various combinations of scripts comprising tri-script and multiscript scenarios. The dataset used in the competition comprised ten different scripts. In total, six systems were received from five participants over the tasks offered. This report details the competition dataset specifications, evaluation criteria, summary of the participating systems and their performance across different tasks. The systems submitted by Google Inc. were the winner of the competition for all the tasks, whereas the systems received from Huazhong University of Science and Technology (HUST) and computer vision Center (CVC) were very close competitors.

关键词： Video scripts identification multi-lingual OCR multi-lingual video-text competition

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Indian multi-script full pin-code string recognition for postal automation

Indian multi-script full pin-code string recognition for pos...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Pal, U. Roy, R.K. Roy, K. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Dept. of Comp. Sc. West Bengal State University Barasat India Graduate School of Engg. Mie University 1577 Kurimamachiya-cho Tsu 514-8507 Japan

ISBN: (纸本)9780769537252

Under three-language formula, the destination address block of postal document of an Indian state is generally written in three languages: English, Hindi and the State official language. Because of inter-mixing of these scripts in postal address writings, it is very difficult to identify the script by which a pin-code is written. Also, because of the writing style of different individuals some of the digits in a pin-code string may touch with its neighboring digits. Accurate segmentation of such touching components into individual digits is a difficult task. To avoid such difficulties, in this paper we proposed a tri-lingual (English, Hindi and Bangla) 6-digit full pin-code string recognition. We obtained 99.01% reliability from our proposed system when error and rejection rates are 0.83% and 15.27%, respectively. © 2009 IEEE.

关键词： Codes (symbols)

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Date field extraction in handwritten documents

Date field extraction in handwritten documents

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International Conference on pattern recognition

作者： Ranju Mandal Partha Pratim Roy Umapada Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Laboratoire dInformatique Université François Rabelais Tours France

Automatic extraction of date patterns from handwritten document involves difficult challenges due to writing styles of different individuals, touching characters and confusion among identification of alphabets and digits. In this paper, we propose a framework for retrieval of date patterns from handwritten documents. The method first classifies word components of each text line into month and non-month class using word level feature. Next, non-month words are segmented into individual components and classified into one of alphabet, digit or punctuation. Using this information of word and character level components, the date patterns are searched first using voting approach and then we detect the candidate lines for numeric and semi-numeric date using regular expression. Gradient based features and Support Vector Machine (SVM) are used in our work for classification. The experiment is performed on handwritten dataset and we have obtained encouraging results from it.

关键词： Support vector machines Feature extraction Handwriting recognition pattern matching Character recognition Image segmentation

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Compound attention embedded dual channel encoder-decoder for ms lesion segmentation from brain MRI

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Multimedia Tools and Applications 2024年 1-33页

作者： Ghosal, Palash Roy, Abhijit Agarwal, Rohit Purkayastha, Kunal Sharma, Aaditya Lochan Kumar, Amish Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Majhitar Sikkim Rangpo737136 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Majhitar Sikkim Rangpo737136 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute West Bengal Kolkata700108 India

Multiple Sclerosis (MS) lesions’ segmentation is difficult due to their variegated sizes, shapes, and intensity levels. Besides this, the class imbalance problem and the availability of limited annotated data samples obstruct the building of highly efficient deep learning-based models. Though researchers have made many attempts to design efficient deep learning-based models, the maximum Dice Coefficient achieved by their models is fairly below the acceptable level of 0.70. The possible reason may be due to the inability to capture sufficient local and global features of the lesions required for accurate segmentation. In this paper, we present a new deep-learning architecture based on compound attention for MS lesion segmentation from magnetic resonance images that handles the challenges of capturing the local and global variable features of the MS lesions. The proposed model is equipped with a dual-channel CNN encoder-decoder structure employing residual connections in one channel and residual channel and spatial attention in the other. The residual connections alleviate the vanishing gradient problem and pass the fine-grained information through the channels, which is crucial for pixel-wise prediction. The attention mechanism used in a channel helps to capture long-range dependencies. Thus, the complete model leverages rich global and local information through the two channels for lesion segmentation. The problem of data imbalance is handled by using the Focal Tversky loss function. Through rigorous evaluation using 3-fold cross-validation on the MICCAI 2016 challenge dataset, our model demonstrates superior performance, achieving a Dice Coefficient of 0.73, surpassing state-of-the-art models in both qualitative and quantitative assessments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

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A New Method for Arbitrarily-Oriented Text Detection in Video

A New Method for Arbitrarily-Oriented Text Detection in Vide...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Nabin Sharma Palaiahnakote Shivakumara Umapada Pal Michael Blumenstein Chew Lim Tan Griffith University QLD Australia School of Computing National University of Singapore Singapore Computer Vision and Pattern Recognition Unit Indian Statistical Unit Kolkata India

Text detection in video frames plays a vital role in enhancing the performance of information extraction systems because the text in video frames helps in indexing and retrieving video efficiently and accurately. This paper presents a new method for arbitrarily-oriented text detection in video, based on dominant text pixel selection, text representatives and region growing. The method uses gradient pixel direction and magnitude corresponding to Sobel edge pixels of the input frame to obtain dominant text pixels. Edge components in the Sobel edge map corresponding to dominant text pixels are then extracted and we call them text representatives. We eliminate broken segments of each text representatives to get candidate text representatives. Then the perimeter of candidate text representatives grows along the text direction in the Sobel edge map to group the neighboring text components which we call word patches. The word patches are used for finding the direction of text lines and then the word patches are expanded in the same direction in the Sobel edge map to group the neighboring word patches and to restore missing text information. This results in extraction of arbitrarily-oriented text from the video frame. To evaluate the method, we considered arbitrarily-oriented data, non-horizontal data, horizontal data, Hua's data and ICDAR-2003 competition data (Camera images). The experimental results show that the proposed method outperforms the existing method in terms of recall and f-measure.

关键词： Cameras Image edge detection Feature extraction Classification algorithms Image resolution Educational institutions pattern recognition

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Comparative Study of Devnagari Handwritten Character recognition Using Different Feature and Classifiers

Comparative Study of Devnagari Handwritten Character Recogni...

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International Conference on Document Analysis and recognition

作者： Umapada Pal Tetsushi Wakabayashi Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering TSU Mie University Mie Japan

In recent years research towards Indian handwritten character recognition is getting increasing attention. Many approaches have been proposed by the researchers towards handwritten Indian character recognition and many recognition systems for isolated handwritten numerals/characters are available in the literature. To get idea of the recognition results of different classifiers and to provide new benchmark for future research, in this paper a comparative study of Devnagari handwritten character recognition using twelve different classifiers and four sets of feature is presented. Projection distance, subspace method, linear discriminant function, support vector machines, modified quadratic discriminant function, mirror image learning, Euclidean distance, nearest neighbour, k-Nearest neighbour, modified projection distance, compound projection distance, and compound modified quadratic discriminant function are used as different classifiers. Feature sets used in the classifiers are computed based on curvature and gradient information obtained from binary as well as gray-scale images.

关键词： Character recognition Handwriting recognition Shape pattern recognition Support vector machines Support vector machine classification Gray-scale Mirrors Machine learning Euclidean distance

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ISIgraphy: A tool for online handwriting sample database generation

ISIgraphy: A tool for online handwriting sample database gen...

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National Conference on computer vision, pattern recognition, Image Processing and Graphics (NCVPRIPG)

作者： Arindam Das Ujjwal Bhattacharya Department of Computer Science & Engineering Narula Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute (ISI) Kolkata India

Online handwriting recognition research has recently received significant thrust. Specifically for Indian scripts, handwriting recognition has not been focused much till in the near past. However, due to generous Government funding through the group on Technology Development for Indian Languages (TDIL) of the Ministry of Communication & Information Technology (MC&IT), Govt. of India, research in this area has received due attention and several groups are now engaged in research and development works for online handwriting recognition in different Indian scripts. An extensive bottleneck of the desired progress in this area is the difficulty of collection of large sample databases of online handwriting in various scripts. Towards the same, recently a user-friendly tool on Android platform has been developed to collect data on handheld devices. This tool is called ISIgraphy and has been uploaded in the Google Play for free download. This application is designed well enough to store handwritten data samples in large scales in user-given file names for distinct users. Its use is script independent, meaning that it can collect and store handwriting samples written in any language, not necessarily an Indian script. It has an additional module for retrieval and display of stored data. Moreover, it can directly send the collected data to others via electronic mail.

关键词： Databases Handwriting recognition Character recognition Handheld computers Benchmark testing Text recognition

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