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LBP Based Line-Wise Script Identification

LBP Based Line-Wise Script Identification

引用

International Conference on Document Analysis and recognition

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

ISBN: (纸本)9781479901937

Script identification is an important step in multi-script document analysis. As different textures present in text portion of a script are the main distinct features of the script, in this paper, we proposed a new algorithm for printed script identification based on texture analysis. Since local patterns is a unifying concept for traditional statistical and structural approaches of texture analysis, here the basic idea is to use the histogram of the local patterns as description of the script stroke directions distribution which is the characteristic of every script. As local pattern, the basic version of the Local Binary patterns (LBP) and a modified version of the Orientation of the Local Binary patterns (OLBP) are proposed. A Least Square Support Vector Machine (LS-SVM) is used as identifier. The scheme has been verified on two databases. The first or training database is a database with 200 sheets of 10 different scripts. The scripts font is provided by the Google translator. The second or test database has been obtained by scanning different newspapers and books. It contains 5 common scripts among 10 different scripts of the first database. From the experiment we obtained encouraging results.

关键词： Databases Histograms Training Testing Support vector machines Image segmentation Feature extraction

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ONLINE ADAPTIVE DICTIONARY LEARNING AND WEIGHTED SPARSE CODING FOR ABNORMALITY DETECTION

ONLINE ADAPTIVE DICTIONARY LEARNING AND WEIGHTED SPARSE CODI...

引用

IEEE International Conference on Image Processing

作者： Sheng Han Ruiqing Fu Suzhen Wang Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences The Chinese University of Hong Kong

ISBN: (纸本)9781479923427

This paper focuses mainly on adaptive dictionary updating and abnormality detection via weighted space coding in video surveillance. Generally, abnormality analysis conducted on a large amount of video data is very complicated, time-consuming and time-variant. However, our dictionary is very efficient at following up on shifted contents in video and abandoning old inactive information in time. The adaptability characteristic also helps reduce the dictionary's size to a small scale, since it only needs to keep recent or active information. We also introduce a simple, but effective, judgement criterion for abnormal detection based on sparse coding over weighted bases. Because of the condensed dictionary and the simplified judgment criterion, our algorithm performs online learning and online detection with a high speed and a high accuracy in various scenes.

关键词： Dictionary Learning Adaptive Learning Sparse Coding Abnormality Detection Sparse coding anomaly detection video data Dictionaries as Topic video monitoring

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Sclera recognition using dense-SIFT

Sclera recognition using dense-SIFT

引用

International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Abhijit Das Umapada Pal Miguel Angel Ferrer Ballester Michael Blumenstein Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Islas Canarias ES

In this paper we propose a biometric sclera recognition and validation system. Here the sclera segmentation is performed bya time-adaptive active contour-based region growing technique. The sclera vessels are not prominent so image enhancement is required and hence a bank of 2D decomposition. A Haar wavelet multi-resolution filter is used to enhance the vessels pattern for better accuracy. For feature extraction, Dense Scale Invariant Feature Transform (D-SIFT) is used. D-SIFT patch descriptors of each training image are used to form bag of features by using k-means clustering and a spatial pyramid model, 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. Anencouraging Equal Error Rate (EER) of 0.66% is attained in the experiments presented.

关键词： Image segmentation Image recognition Image resolution Government Biomedical imaging Robustness

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ICDAR 2013 CROHME: Third International Competition on recognition of Online Handwritten Mathematical Expressions

ICDAR 2013 CROHME: Third International Competition on Recogn...

引用

International Conference on Document Analysis and recognition

作者： Harold Mouchère Christian Viard-Gaudin Richard Zanibbi Utpal Garain Dae Hwan Kim Jin Hyung Kim LUNAM IRCCyN IVC-UMR CNRS 6597 Ecole Polytechnique de Université de Nantes Nantes France Department of Computer Science Rochester Institute of Technology Rochester NY USA Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (纸本)9781479901937

We report on the third international Competition on Handwritten Mathematical Expression recognition (CROHME), in which eight teams from academia and industry took part. For the third CROHME, the training dataset was expanded to over 8000 expressions, and new tools were developed for evaluating performance at the level of strokes as well as expressions and symbols. As an informal measure of progress, the performance of the participating systems on the CROHME 2012 data set is also reported. Data and tools used for the competition will be made publicly available.

关键词： Grammar Handwriting recognition Training Training data Layout Mathematical model

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Recent advances in video based document processing: A review

Recent advances in video based document processing: A review

引用

10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Sharma, Nabin Pal, Umapada Blumenstein, Michael Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Unit Kolkata India

ISBN: (纸本)9780769546612

Extraction and recognition of text present in video has become a very popular research area in the last decade. Generally, text present in video frames is of different size, orientation, style, etc. with complex backgrounds, noise, low resolution and contrast. These factors make the automatic text extraction and recognition in video frames a challenging task. A large number of techniques have been proposed by various researchers in the recent past to address the problem. This paper presents a review of various state-of-the-art techniques proposed towards different stages (e.g. detection, localization, extraction, etc.) of text information processing in video frames. Looking at the growing popularity and the recent developments in the processing of text in video frames, this review imparts details of current trends and potential directions for further research activities to assist researchers. © 2012 IEEE.

关键词： Extraction

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Text independent writer identification for Oriya script

Text independent writer identification for Oriya script

引用

10th IAPR International Workshop on Document Analysis Systems, DAS 2012

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

ISBN: (纸本)9780769546612

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. © 2012 IEEE.

关键词： Support vector machines

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Text detection of two major Indian scripts in natural scene images

Text detection of two major Indian scripts in natural scene ...

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4th International Workshop on Camera-Based Document Analysis and recognition, CBDAR 2011

作者： Roy Chowdhury, Aruni Bhattacharya, Ujjwal Parui, Swapan K. Department of Information Technology Heritage Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783642293634

In this article, we present a robust scheme for detection of Devanagari or Bangla texts in scene images. These are the two most popular scripts in India. The proposed scheme is primarily based on two major characteristics of such texts - (i) variations in stroke thickness for text components of a script are low compared to their non-text counterparts and (ii) presence of a headline along with a few vertical downward strokes originating from this headline. We use the Euclidean distance transform to verify the general characteristics of texts in (i). Also, we apply the probabilistic Hough line transform to detect the characteristic headline of Devanagari and Bangla texts. Further, similarity and adjacency measures are applied to identify text regions, which do not satisfy the verification in (ii). The proposed approach has been simulated on a repository of 120 images taken from Indian roads and the results are encouraging. Also, we have discussed the applicability of the proposed scheme for detection of English texts. Towards this end, we have considered the training and test samples from the image database of ICDAR 2003 Robust Reading Competition. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Character recognition

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Contextual Suggestion 21

Contextual Suggestion

引用

21st Text REtrieval Conference, TREC 2012

作者： Mallik, Abhishek Mitra, Mandar Ghosh, Kripabandhu Department of Computer Science and Enginerring IIT Bombay Powai Mumbai400076 India Computer Vision and Pattern Recognition Unit ISI Kolkata Kolkata700 108 India

The goal of this project is to build a Contextual Suggestion system that will recommend the usel a lanked list of suggestions depending on user's context as well as her preferences. In this context we prent an algorithm based on the regular expression for *** time and address information from different websites for the places. Then we have suggested a ranking function that can utilize these timing and address information of those places as well as the users' context and preferences to rank the places. Here the page6 are unstructured but the address is structured i.e. template based as we have worked with only the plaaes located at Toronto, Canada. © 2012 21st Text REtrieval Conference, TREC 2012. All rights reserved.

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Bangla date field extraction in offline handwritten documents 12

Bangla date field extraction in offline handwritten document...

引用

Workshop on Document Analysis and recognition, DAR 2012

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

ISBN: (纸本)9781450317979

Date is a useful information for various application (e.g. date wise document indexing) and automatic extraction of date information involves difficult challenges due to writing styles of different individuals, touching characters and confusion among identification of numerals, punctuation and texts. In this paper, we present a framework for indexing/retrieval of Bangla 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 text, digit or punctuation. Using this information of word and character level components, the date patterns are searched. First using voting approach and then using regular expression we detect the candidate lines for numeric and semi-numeric date. Dynamic Time Warping (DTW) matching of profile based features is used for classification of month/non-month words. Numerals and punctuations are classified using gradient based feature and SVM classifier. The experiment is performed on Bangla handwritten dataset and the results demonstrate the effectiveness of the proposed system. © 2012 ACM.

关键词： Extraction

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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2012 19th International Conference on Systems, Signals and Image Processing, IWSSIP 2012

作者： Shalunts, Gayane Haxhimusa, Yll Sablatnig, Robert Institute of Computer Aided Automation Computer Vision Lab. Vienna University of Technology Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab. Vienna University of Technology Austria

ISBN: (纸本)9783200023284

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate. © 2012 Institute of Telecommunica.

关键词： Classification (of information)

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