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Flexible Sequence Matching Technique: Application to Word Spotting in Degraded Documents

Flexible Sequence Matching Technique: Application to Word Sp...

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International Workshop on Frontiers in Handwriting recognition

作者： Tanmoy Mondal Nicolas Ragot Jean-Yves Ramel Umapada Pal Université François Rabelais Laboratoire d'Informatique Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, a new sequence-matching algorithm, called as Flexible Sequence Matching (FSM) algorithm is proposed. FSM combines several abilities of other sequence matching algorithms (especially DTW, CDP and MVM) that could be configured depending on the application domain. Its generality and robustness comes from its ability to find sub sequences (as in CDP), to skip outliers inside the match sequences (as in MVM) and to match multiple elements with a single one (as in CDP and DTW). These properties make it extremely suitable for robust word spotting. More precisely, the FSM algorithm has the capability to retrieve a query inside a line or piece of line. This facility is useful as word segmentation process may not work accurately or when only line segmentation information is available. Furthermore, thanks to its skipping capability, that makes the proposed FSM algorithm less sensible to local variations in the spelling of words, and also to local degradation effects. Finally, its multiple matching facilities (many to one and one to many matching) are useful in case of different length of target and query sequences due to the variability in scale factor. We demonstrate the superiority of proposed FSM algorithm in specific cases such as incorrect word segmentation and word level local variations. When different experiments were performed using handwritten George Washington dataset and also on historical typewritten document images, quite promising results were obtained.

关键词： Feature extraction Algorithm design and analysis Image segmentation computer architecture Robustness pattern recognition Dynamic programming

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Saliency attention based abnormal event detection in video

Saliency attention based abnormal event detection in video

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IEEE International Conference on Robotics and Biomimetics

作者： Wang Huan Huiwen Guo Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition University of Chinese Academy of Sicences Department of Mechanical and Automation Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781479973989

Most existing methods for abnormal event detection in the literature are relied on a training phase. Different from conventional approaches for abnormal event detection, a saliency attention based abnormal event detection approach is proposed in this paper. It is inspired by the visual attention mechanism that abnormal events are those which attract attention mostly in videos. The temporal and spatial abnormal saliency maps are firstly constructed and then the final abnormal event map is formatted by fusing them using a method with dynamic coefficients. The temporal abnormal saliency map is constructed by motion contrast between keypoints extracted from two successive video frames. The spatial abnormal saliency map is structured based on the color contrasts. Experiments performed on the benchmark datasets show that the proposed method achieves a high accurate and robust results for abnormal event detection without a training phase.

关键词： Clustering algorithms Hidden Markov models Event detection Conferences Color computer vision pattern recognition

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ICFHR 2014 Competition on recognition of On-Line Handwritten Mathematical Expressions (CROHME 2014)

ICFHR 2014 Competition on Recognition of On-Line Handwritten...

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International Workshop on Frontiers in Handwriting recognition

作者： H. Mouchère C. Viard-Gaudin R. Zanibbi U. Garain IRCCyN/IVC LUNAM Université de Nantes Department of Computer Science Rochester Institute of Technology Rochester NY USA Computer Vision and Pattern Recognition (CVPR) Indian Statistical Institute

We present the outcome of the latest edition of the CROHME competition, dedicated to on-line handwritten mathematical expression recognition. In addition to the standard full expression recognition task from previous competitions, CROHME 2014 features two new tasks. The first is dedicated to isolated symbol recognition including a reject option for invalid symbol hypotheses, and the second concerns recognizing expressions that contain matrices. System performance is improving relative to previous competitions. Data and evaluation tools used for the competition are publicly available.

关键词： Feature extraction Training Handwriting recognition Grammar Mathematical model Layout Ink

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Keyword spotting for self-training of BLSTM NN based handwriting recognition systems

Keyword spotting for self-training of BLSTM NN based handwri...

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作者： Frinken, Volkmar Fischer, Andreas Baumgartner, Markus Bunke, Horst Computer Vision Center Autonomous University of Barcelona Edifici O 08193 Bellaterra Barcelona Spain Centre for Pattern Recognition and Machine Intelligence Concordia University 1455 de Maisonneuve Blvd West Montreal QC H3G 1M8 Canada Institute of Computer Science and Applied Mathematics University of Bern Neubrückstrasse 10 CH-3012 Bern Switzerland

The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. © 2013 Elsevier Ltd. All rights reserved.

关键词： Character recognition

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Design of Unsupervised Feature Extraction System for On-line Bangla Handwriting recognition

Design of Unsupervised Feature Extraction System for On-line...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Volkmar Frinken Nilanjana Bhattacharya Umapada Pal Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka-shi Japan Bose Institute Kolkatta India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkatta India

Different systems for handwriting recognition use different features to represent the input text. Even after decades of research, no favorable decision on a best-practice exists and many features are carefully hand-crafted. To facilitate the design phase for on-line handwriting systems, in this paper, we propose an unsupervised feature generation approach based on dissimilarity space embedding (DSE) of local neighborhoods around the points along the trajectory. DSE has high capability of discriminative representation and hence beneficial for classification. We compare the approach with a state-of-the-art feature extraction method and demonstrate its superiority.

关键词： Feature extraction Prototypes Handwriting recognition Writing Hidden Markov models Character recognition Text recognition

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Multi-modal pipeline for comprehensive validation of Mitral Valve geometry and functional computational models

Multi-modal pipeline for comprehensive validation of Mitral ...

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4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013

作者： Neumann, Dominik Grbic, Sasa Mansi, Tommaso Voigt, Ingmar Rabbah, Jean-Pierre Siefert, Andrew W. Saikrishnan, Neelakantan Yoganathan, Ajit P. Yuh, David D. Ionasec, Razvan Ioan Imaging and Computer Vision Siemens Corporate Technology Princeton NJ United States Pattern Recognition Lab. University of Erlangen-Nuremberg Germany Computer Aided Medical Procedures Technical University Munich Germany Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta GA United States Section of Cardiac Surgery Department of Surgery Yale University New Haven CT United States

ISBN: (纸本)9783642542671

Valvular heart disease affects a high number of patients, exhibiting significant mortality and morbidity rates. Mitral Valve (MV) Regurgitation, a disorder in which the MV does not close properly during systole, is among its most common forms. Traditionally, it has been treated with MV replacement. However, recently there is an increased interest in MV repair procedures, providing better long-term survival, better preservation of heart function, lower risk of complications, and usually eliminating the need for long-term use of blood thinners (anticoagulants). These procedures are complex and require an experienced surgeon and elaborate pre-operative planning. Hence, there is a need for efficient tools for training and planning of MV repair interventions. Computational models of valve function have been developed for these purposes. Nevertheless, state-of-the-art models remain approximations of real anatomy with considerable simplifications, since current modalities are limited by image quality. Hence, there is an important need to validate such low-fidelity models against comprehensive ex-vivo data to assess their clinical applicability. As a first step towards this aim, we propose an integrated pipeline for the validation of MV geometry and function models estimated in ex-vivo TEE data with respect to ex-vivo microCT data. We utilize a controlled experimental setup for ex-vivo imaging and employ robust machine learning and optimization techniques to extract reproducible geometrical models from both modalities. Using one exemplary case, we demonstrate the validity of our framework. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Pipelines

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Offline Hand-Written Musical Symbol recognition

Offline Hand-Written Musical Symbol Recognition

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International Workshop on Frontiers in Handwriting recognition

作者： Sukalpa Chanda Debleena Das Umapada Pal Fumitaka Kimura Department of Computer Science and Media Technology Gjøvik University College Gjøvik Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University TSU Japan

recognition of offline musical symbols can aid in automatic retrieval of a particular piece of musical notation from a digital repository. Though some work on on-line Musical symbol notations exists, little work has been done on off-line recognition of the symbols. This article proposes a system for offline isolated musical symbol recognition. Efficacy of a texture analysis based feature extraction method is compared with a structural shape descriptor based feature extraction method coupled with a Support Vector Machine (SVM) classifier. Later three different kinds of feature selection techniques were also analyzed to gauge the contribution of each feature in the overall classification process. We compared our results with an existing method and we noted the proposed system exhibited encouraging results and it is better than existing method. The proposed system even worked better when we used MQDF classifier in place of SVM. In a five-fold cross validation experimental framework, considering 3795 music symbols we achieved 97.50% and 98.05% accuracy from SVM and MQDF classifiers, respectively when chain-code histogram features are applied.

关键词： Feature extraction Accuracy Support vector machines Histograms Handwriting recognition Educational institutions Classification algorithms

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Stroke Level User-Adaptation for Stroke Order Free Online Handwriting recognition

Stroke Level User-Adaptation for Stroke Order Free Online Ha...

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International Workshop on Frontiers in Handwriting recognition

作者： D. Dutta A. Roy Chowdhury U. Bhattacharya S.K. Parui Dept. of Information Technology Heritage Institute of Technology Kolkata India Heritage Institute of Technology Kolkata West Bengal IN Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this article, a novel lightweight user-adaptive online handwriting recognition scheme has been presented. The present recognition approach is stroke order free. It is based on prior identification of the set of various strokes of different shapes used in writing the characters of the underlying alphabet utilizing a representative sample database. In this approach a very small number of prototypes of each stroke shape is used along with certain weighted DTW distance based nearest neighbour classifier to recognize the strokes in the input character. Individual characters are identified using a look-up table (LUT) each row of which corresponds to one character composed of a distinct set of stroke shapes. This LUT is formed using the representative sample set of the underlying character set. If a stroke does not find a close match in the training set or if the set of strokes for an input character does not find a corresponding entry in the LUT, user adaptation takes place using a modified Learning Vector Quantization (LVQ) method. The proposed scheme has been implemented in an Android-based Bangla handwriting recognizer [1] for handheld devices and its recognition performance is encouraging.

关键词： Handwriting recognition Character recognition Libraries Prototypes Table lookup Accuracy Writing

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Automatic image-to-model framework for patient-specific electromechanical modeling of the heart

Automatic image-to-model framework for patient-specific elec...

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IEEE International Symposium on Biomedical Imaging

作者： Dominik Neumann Tommaso Mansi Sasa Grbic Ingmar Voigt Bogdan Georgescu Elham Kayvanpour Ali Amr Farbod Sedaghat-Hamedani Jan Haas Hugo Katus Benjamin Meder Joachim Hornegger Ali Kamen Dorin Comaniciu Imaging and Computer Vision Siemens Corporate Technology Princeton NJ Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Internal Medicine III University Hospital Heidelberg Germany

A key requirement for recent advances in computational modeling to be clinically applicable is the ability to fit models to patient data. Various personalization techniques have been proposed for isolated sub-components of complex models of heart physiology. However, no work has been presented that focuses on personalizing full electromechanical (EM) models in a streamlined, consistent and automatic fashion, which has been evaluated on a large population. We present an integrated system for full EM personalization from routinely acquired clinical data. The importance of mechanical parameters is analyzed in a comprehensive sensitivity study, revealing that myocyte contraction and Young's modulus are the main determinants of model output variation, what lead to the proposed personalization strategy. On a large, physiologically diverse set of 15 patients, we demonstrate the effectiveness of our framework by comparing measured and calculated parameters, yielding left ventricular ejection fraction and stroke volume errors of 6.6% and 9.2 mL, respectively.

关键词： Computational modeling Biological system modeling Heart Solid modeling Data models Electrocardiography Biomechanics

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Automatic Handwritten Indian Scripts Identification

Automatic Handwritten Indian Scripts Identification

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International Workshop on Frontiers in Handwriting recognition

作者： Rajmohan Pardeshi B.B. Chaudhuri Mallikarjun Hangarge K.C. Santosh Department of Computer Science Karnatak Arts Science & Commerce College Bidar INDIA Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata INDIA US National Library of Medicine (NLM) National Institutes of Health (NIH) Bethesda MD USA

Since OCR engines are usually script-dependent, automatic text recognition in multi-script document requires a pre-processor module that identifies the scripts. Based on this motivation, in this paper, we present a word level handwritten Indian script identification technique. To handle this, words are first segmented by morphological dilation and performed connected component labelling. We then employ the Radon transform, discrete wavelet transform, statistical filters and discrete cosine transform to extract the directional multi-resolution spatial features. We tested the features by using linear discriminant analysis, support vector machine and K-nearest neighbour classifiers over 11 different major Indian scripts (including Roman) in bi-script and tri-script scenario. In our tests, we have achieved maximum accuracies of 98% and 96% for bi-script and tri-scipt respectively.

关键词： Discrete cosine transforms Discrete wavelet transforms Support vector machines Accuracy Feature extraction Kernel

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