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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...

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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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Text independent writer identification for Oriya script

Text independent writer identification for Oriya script

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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

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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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Lexicon reduction technique for Bangla handwritten word recognition

Lexicon reduction technique for Bangla handwritten word reco...

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

作者： Bhowmik, Tapan Kumar Roy, Utpal Parui, Swapan K. Faculty of Mathematics and Natural Sciences University of Groningen Netherlands Department of Computer and System Sciences Visva Bharati Santiniketan India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9780769546612

In this paper we introduce a stroke based lexicon reduction technique in order to reduce the search space for recognition of handwritten words. The principle of this technique involves mainly two aspects of a word image to constitute a feature vector: one is word-length and the other is shape of the word. The length of the word image is represented by the number of specific vertical strokes present in the word image and, on the other hand, the shape of a word image is realized with the combination of both horizontal and vertical strokes. The experiment has been carried out with a database of 35,700 off-line handwritten Bangla word images. Though our proposed lexicon reduction technique is developed for recognition of Bangla handwritten words, its generalization property can easily be exploited for recognition of handwriting in other scripts also. © 2012 IEEE.

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

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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Font identification — In context of an Indic script

Font identification — In context of an Indic script

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International Conference on pattern recognition

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

ISBN: (纸本)9781467322164

Font can be used as a notion of similarity amongst multiple documents written in same script. We could automatically retrieve document images with specific font from a huge digital document repository. So Optical Font recognition could be a useful pre-processing step in an automated questioned document analysis system for sorting documents with similar fonts. We propose a scheme to identify 10 different fonts for an Indic script (Bangla). Curvature-based features are extracted from segmented characters and are fed to a Support Vector Machine (SVM) classifier. The classifier determines the font type for each segmented character obtained from a document. Later, font identification for that document is executed on the basis of majority voting amongst 10 different fonts for all characters. Using a Multiple Kernel SVM classifier we obtained 98.5% accuracy from 400 test documents (40 documents for each font type).

关键词： Support vector machines Kernel Accuracy Feature extraction Training Image segmentation 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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Online non-feedback image re-ranking via dominant data selection

Online non-feedback image re-ranking via dominant data selec...

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20th ACM International Conference on Multimedia, MM 2012

作者： Cao, Chen Chen, Shifeng Li, Yuhong Liu, Jianzhuang Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab. Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450310895

Image re-ranking aims at improving the precision of keyword-based image retrieval, mainly by introducing visual features to re-rank. Many existing approaches require offline training for every keyword, which are unsuitable for online image search. Other real-time approaches demand user interaction, which are inappropriate for large-scale image collection. To improve the accuracy of web image retrieval in a practicable manner, we propose a novel re-ranking algorithm to explore the cluster information of the image set. First, we build spectral graph on images that retrieved bysearch engine, and remove isolated nodes as noisy images. Then, we select positive samples from the most dominant cluster in initial top-ranked images, and the samples are used for semi-supervised learning and ranking. Our algorithm is online and non-feedback. Experiments on two public databases demonstrate that our algorithm outperforms the state-of-the-art approaches. © 2012 ACM.

关键词： Supervised learning

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