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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...

引用

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

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

引用

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

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

引用

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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Offline Hand-Written Musical Symbol recognition

Offline Hand-Written Musical Symbol Recognition

引用

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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Gradient-Angular-Features for Word-wise Video Script Identification

Gradient-Angular-Features for Word-wise Video Script Identif...

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International Conference on pattern recognition

作者： Palaiahnakote Shivakumara Nabin Sharma Umapada Pal Michael Blumenstein Chew Lim Tan Faculty of Computer Science University of Malaya Kuala Lumpur Malaysia 'Griffith University Gold Coast Queensland Australia Cornputcr Vision and Pattern Recognition Unit Indian Statistical Institute India School of Computing National University of Singapore Singapore

ISBN: (纸本)9781479952106

Script identification at the word level is challenging because of complex backgrounds and low resolution of video. The presence of graphics and scene text in video makes the problem more challenging. In this paper, we employ gradient angle segmentation on words from video text lines. This paper presents new Gradient-Angular-Features (GAF) for video script identification, namely, Arabic, Chinese, English, Japanese, Korean and Tamil. This work enables us to select an appropriate OCR when the frame has words of multi-scripts. We employ gradient directional features for segmenting words from video text lines. For each segmented word, we study the gradient information in effective ways to identify text candidates. The skeleton of the text candidates is analyzed to identify Potential Text Candidates (PTC) by filtering out unwanted text candidates. We propose novel GAF for the PTC to study the structure of the components in the form of cursiveness and softness. The histogram operation on the GAF is performed in different ways to obtain discriminative features. The method is evaluated on 760 words of six scripts having low contrast, complex background, different font sizes, etc. in terms of the classification rate and is compared with an existing method to show the effectiveness of the method. We achieve 88.2% average classification rate.

关键词： Feature extraction Histograms Equations Mathematical model Shape Support vector machine classification pattern recognition

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Improving ir performance from ocred text using cooccurrence 13

Improving ir performance from ocred text using cooccurrence

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5th International Workshop on Forum for Information Retrieval Evaluation, FIRE 2013

作者： Ghosh, Kripabandhu Chakraborty, Anirban Parui, Swapan Kumar Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata West Bengal India

ISBN: (纸本)9781450328302

Information Retrieval performance is hurt to a great extent by OCR errors. Much research has been reported on mod- elling and correction of OCR errors. However, all the ex- isting systems make use of language dependent resources or training texts to study the nature of errors. No research has been reported on improving retrieval performance from erro- neous text when no training data is available. We propose a novel algorithm for automatic detection of OCR errors and improvement of retrieval performance from the erroneous corpus. Our algorithm does not use any training data or any language specific resources like thesaurus. It also does not use any knowledge about the language except that the word delimiter is blank space. We have tested our algo- rithm on erroneous OCRed Bangla FIRE collection offered in the RISOT 2012 track and obtained about 9% improvement over the OCRed baseline. However, the improvement is not statistically significant. © 2013 ACM.

关键词： Errors

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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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Multiple Training - One Test Methodology for Handwritten Word-Script Identification

Multiple Training - One Test Methodology for Handwritten Wor...

引用

International Workshop on Frontiers in Handwriting recognition

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

Script identification is an important area in handwriting document image analysis field. The script identification at word level on documents written in multiple scripts is an open challenge for the scientific community and a real concern in countries with multiple official languages, e. G. The country like India. Such documents usually contain two scripts: the most of the document are written in the regional script while some words, acronyms or numbers are written in Roman script. In this case a word or even a character level script identification is required to locate the second script characters in the document. Here the major problem is the few script descriptors available for the script estimation which convey high error rates. The literatures try to address this problem by looking for more efficient descriptors. In this paper we propose a Multiple Training - One Test technique to alleviate this problem. Several classifiers are trained, each one with words of similar amount of information. A scale invariable word information index is defined for this sake. To identify the script of a query word, its word information index is worked out, and its script is identified with the most appropriate classifier. Accuracy improvements has been obtained with this promising technique, especially for the shorten words.

关键词： Training Histograms Feature extraction Indexes Accuracy Testing

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Multi-angle based lively sclera biometrics at a distance

Multi-angle based lively sclera biometrics at a distance

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IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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 IDeTIC Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Islas Canarias ES

This piece of work proposes a liveliness based sclera eye biometric, validation and recognition technique at a distance. The images in this work are acquired by a digital camera in the visible spectrum at varying distance of about 1 meter from the individual. Each individual during registration as well as validation is asked to look straight and move their eye ball up, left and right keeping their face straight to incorporate liveliness of the data. At first the image is divided vertically into two halves and the eyes are detected in each half of the face image that is captured, by locating the eye ball by a Circular Hough Transform. Then the eye image is cropped out automatically using the radius of the iris. Next a C-means-based segmentation is used for sclera segmentation followed by vessel enhancement by the adaptive histogram equalization and Haar filtering. The feature extraction was performed by patch-based Dense-LDP (Linear Directive pattern). Furthermore each training image is used to form a bag of features, which is used to produce the training model. Each of the images of the different poses is combined at the feature level and the image level to obtain higher accuracy and to incorporate liveliness. The fusion that produces the best result is considered. Support Vector Machines (SVMs) are used for classification. Here images from 82 individuals (both left and right eye i.e. 164 different eyes) are used and an appreciable Equal Error Rate of 0.52% is achieved in this work.

关键词： Iris recognition Image segmentation Feature extraction Training Histograms Adaptive equalizers

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Fuzzy logic based selera recognition

Fuzzy logic based selera recognition

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

In this paper a sclera recognition and validation system is proposed. Here sclera segmentation was performed by Fuzzy logic-based clustering. Since the selera vessels are not prominent, image enhancement was required. A Fuzzy logic-based Brightness Preserving Dynamic Fuzzy Histogram Equalization and discrete Meyer wavelet was used to enhance the vessel patterns. For feature extraction, the Dense Local Binary pattern (D-LBP) was used. D-LBP patch descriptors of each training image are used to form a bag of features, 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. An encouraging Equal Error Rate (EER) of 4.31% was achieved in our experiments.

关键词： Image segmentation Iris recognition Level set Histograms Training Feature extraction

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