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A probabilistic framework for logo detection and localization in natural scene images

A probabilistic framework for logo detection and localizatio...

引用

International Conference on pattern recognition

作者： Ankush Roy Utpal Garain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9781467322164

This paper presents a probabilistic approach for logo detection and localization in natural scene images. Two probability distributions are computed, one considering the features extracted from the key points located inside a region and the second refers to shape geometry defined by the key points. The barycentric co-ordinates are considered to define the shape statistics. The performance of the proposed approach has been reported on two publicly available datasets: BelgaLogos and Flickr Logos27. It is shown that statistically significant improvement is achieved over a recently proposed method.

关键词： Feature extraction Shape Geometry Accuracy Probabilistic logic Databases Visualization

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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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A System for Handwritten and Machine-Printed Text Separation in Bangla Document Images

A System for Handwritten and Machine-Printed Text Separation...

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International Workshop on Frontiers in Handwriting recognition

作者： Purnendu Banerjee B.B. Chaudhuri Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

In this paper, we describe an approach to distinguish between hand-written text and machine-printed text from annotated machine-printed Bangla Documents images. In applications involving OCR, distinction of machine-printed and hand-written characters is important, so that they can be sent to separate recognition engines. Identification of hand-written parts is useful in deleting those parts and cleaning the document image as well. In this paper a classification system is presented which takes a connected component in the document image and assigns them to two classes namely "machine-printed" and for "hand-written" classes, respectively. The proposed system contains a preprocessing step, which smoothes the object border and finds the Connected Component. Bangla script specific features are extracted from that Connected Component image, and a standard classifier based on SVM generates the final response. Experimental results on a data set show that the proposed approach achieves an overall accuracy of 96.49%.

关键词： Support vector machines Feature extraction Handwriting recognition Accuracy Text recognition Training

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Stroke Segmentation and recognition from Bangla Online Handwritten Text

Stroke Segmentation and Recognition from Bangla Online Handw...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Nilanjana Bhattacharya Umapada Pal Bose Institute Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper deals with recognition of online handwritten Bangla (Bengali) text. Here, at first, we segment cursive words into strokes. A stroke may represent a character or a part of a character. We selected a set of Bangla words written by different groups of people such that they contain all basic characters, all vowel and consonant modifiers and almost all types of possible joining among them. For segmentation of text into strokes, we discovered some rules analyzing different joining patterns of Bangla characters. Combination of online and offline information was used for segmentation. We achieved correct segmentation rate of 97.89% on the dataset. We manually analyzed different strokes to create a ground truth set of distinct stroke classes for result verification and we obtained 85 stroke classes. Directional features were used in SVM for recognition and we achieved correct stroke recognition rate of 97.68%.

关键词： Support vector machines Handwriting recognition Image segmentation Gold Writing Feature extraction Character recognition

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Off-line English and Chinese Signature Identification Using Foreground and Background Features

Off-line English and Chinese Signature Identification Using ...

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International Joint Conference on Neural Networks

作者： Srikanta Pal Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Computer Vision and Pattern Recognition Unit Indian Statistical Institute

ISBN: (纸本)9781467314886

In the field of information security, the usage of biometrics is growing for user authentication. Automatic signature recognition and verification is one of the biometric techniques, which is only one of several used to verify the identity of individuals. In this paper, a foreground and background based technique is proposed for identification of scripts from bi-lingual (English/Roman and Chinese) off-line signatures. This system will identify whether a claimed signature belongs to the group of English signatures or Chinese signatures. The identification of signatures based on its script is a major contribution for multi-script signature verification. Two background information extraction techniques are used to produce the background components of the signature images. Gradient-based method was used to extract the features of the foreground as well as background components. Zernike Moment feature was also employed on signature samples. Support Vector Machine (SVM) is used as the classifier for signature identification in the proposed system. A database of 1120 (640 English+480 Chinese) signature samples were used for training and 560 (320 English+240 Chinese) signature samples were used for testing the proposed system. An encouraging identification accuracy of 97.70% was obtained using gradient feature from the experiment.

关键词： Off-line verification systems Signature identification Biometrics Authentication systems SVM

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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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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

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

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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A Comparative Study of Persian/Arabic Handwritten Character recognition

A Comparative Study of Persian/Arabic Handwritten Character ...

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International Workshop on Frontiers in Handwriting recognition

作者： Alireza Alaei Umapada Pal P. Nagabhushan Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In recent years, many techniques for the recognition of Persian/Arabic handwritten documents have been proposed by researchers. To test the promises of different features extraction and classification methods and to provide a new benchmark for future research, in this paper a comparative study of Persian/Arabic handwritten character recognition using different feature sets and classifiers is presented. Feature sets used in this study are computed based on gradient, directional chain code, shadow, under-sampled bitmap, intersection/junction/endpoint, and line-fitting information. Support Vector Machines (SVMs), Nearest Neighbour (NN), k-Nearest Neighbour (k-NN) are used as different classifiers. We evaluated the proposed systems on a standard dataset of Persian handwritten characters. Using 36682 samples for training, we tested the proposed recognition systems on other 15338 samples and their detailed results are reported. The best correct recognition of 96.91% is obtained in this comparative study.

关键词： Feature extraction Character recognition Handwriting recognition Shape Support vector machines Accuracy Training

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Scene text detection using sparse stroke information and MLP

Scene text detection using sparse stroke information and MLP

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International Conference on pattern recognition

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

In this article, we present a novel set of features for detection of text in images of natural scenes using a multi-layer perceptron (MLP) classifier. An estimate of the uniformity in stroke thickness is one of our features and we obtain the same using only a subset of the distance transform values of the concerned region. Estimation of the uniformity in stroke thickness on the basis of sparse sampling of the distance transform values is a novel approach. Another feature is the distance between the foreground and background colors computed in a perceptually uniform and illumination-invariant color space. Remaining features include two ratios of anti-parallel edge gradient orientations, a regularity measure between the skeletal representation and Canny edgemap of the object, average edge gradient magnitude, variation in the foreground gray levels and five others. Here, we present the results of the proposed approach on the ICDAR 2003 database and another database of scene images consisting of text of Indian scripts.

关键词： Image edge detection Image color analysis Transforms Databases Feature extraction Robustness pattern recognition

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International Conference on Hybrid Intelligent Systems (HIS)

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

Classification/misclassification of similar shaped characters largely affects OCR accuracy. Sometimes occlusion/insertion of a part of character (due to inferior scanning quality) also makes it look alike another character type. For such adverse situations a part based character recognition system could be more effective. In order to encounter mentioned adverse scenario we propose a new feature encoding technique. This feature encoding is based on the amalgamation of Gabor filter-based features with SURF features (G-SURF). Features generated from a character are provided to Support Vector Machine (SVM) classifier. We obtained an encouraging accuracy on similar shaped characters from three different scripts.

关键词： Decision support systems Hybrid intelligent systems Rail to rail outputs

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