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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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Frequency-plane analysis of normal and pathological ECG signals for disease identification

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Journal of Medical Engineering and Technology 2005年第5期29卷 219-227页

作者： Mitra, S. Mitra, M. Chaudhuri, B.B. Department of Applied Physics Faculty of Technology University of Calcutta 92 APC Road Kolkata - 700009 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata - 700 108 India

In this paper a frequency plane analysis of both normal and diseased ECG signals is performed specifically for disease identification. Image processing techniques are used to develop an automated data acquisition package of 12 lead ECG signals from paper records. A regeneration domain is also developed to check the captured pattern with the original wave shape. A QRS complex detector with an accuracy level ∼98.4% in up to 30% signal to noise level is developed. Discrete Fourier transform (DFT) is performed to obtain the frequency spectrum of every ECG signal. Some interesting amplitude and phase response properties of chest lead V2, V3, V4, V6 and limb lead I, II, III, AVL, AVF are seen. Both amplitude and phase properties are different for normal and diseased subjects and can serve an important role in disease identification. A statistical analysis of amplitude property is carried out to show that this property is significantly different for normal and diseased subjects. © 2005 Taylor & Francis.

关键词： Electrocardiography

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A Novel Approach to Skew Detection and Character Segmentation for Handwritten Bangla Words

A Novel Approach to Skew Detection and Character Segmentatio...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： A. Roy T.K. Bhowmik S.K. Parui U. Roy Department of Computer and System Sciences Visva Bharati University Santiniketan India Embassy Golf Link IBM Global Services Private Limited Bangalore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Character segmentation is a necessary preprocessing step for character recognition in many handwritten word recognition systems. The most difficult case in character segmentation is the cursive script. Fully cursive nature of Bangla handwriting, the natural skewness in words poses some challenges for automatic character segmentation. In this article a novel approach to skew detection, correction as well as character segmentation has been presented for handwritten Bangla words as a test case. Segmenting points are extracted on the basis of some patterns observed in the handwritten words. With these segmenting points a graphical path (hereafter referred to as a candidate path) has been constructed. The handwritten words contain some consistent and also inconsistent skewness. Our algorithm can cope with both types of skewness at a time. Further the method is so direct that with the help of a candidate path one can handle both skew correction and segmentation successfully. the algorithm has been tested on a database prepared for laboratory use. The method yields fairly good results for this database.

关键词： Image segmentation Character recognition Optical character recognition software computer vision pattern recognition Testing Databases Algorithm design and analysis Writing Handwriting recognition

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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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DCT-phase statistics for forged IMEI numbers and air ticket detection

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Expert Systems with Applications 2021年 164卷 114014-114014页

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Kanchan, Swati Basavaraja, V. Guru, D.S. Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Studies in Computer Science University of Mysore Karnataka India National Key Lab for Novel Software Technology Nanjing University China Sydney Australia

New tools have been developing with the intention of having more flexibility and greater user-friendliness for editing the images and documents in digital technologies, but, unfortunately, they are also being used for manipulating and tampering information. Examples of such crimes include creating forged International Mobile Equipment Identity (IMEI) numbers which are embedded on mobile packages and inside smart mobile cases for illicit activities. Another example of such crimes is altering the name or date on air tickets for breaching security at the airport. This paper presents a new expert system for detecting forged IMEI numbers as well as altered air ticket images. The proposed method derives the phase spectrum using the Discrete Cosine Transform (DCT) to highlight the suspicious regions;it is unlike the phase spectrum from a Fourier transform, which is ineffective due to power spectrum noise. From the phase spectrum, our method extracts phase statistics to study the effect of distortions introduced by forgery operations. This results in feature vectors, which are fed to a Support Vector Machine (SVM) classifier for detection of forged IMEI numbers and air ticket images. Experimental results on our dataset of forged IMEI numbers (which is created by us for this work), on altered air tickets, on benchmark datasets of video caption text (which is tampered text), and on altered receipts of the ICPR 2018 FDC dataset, show that the proposed method is robust across different datasets. Furthermore, comparative studies of the proposed method with the existing methods on the same datasets show that the proposed method outperforms the existing methods. The dataset created will be available freely on request to the authors. © 2020 Elsevier Ltd

关键词： Discrete cosine transforms

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Music Instrument Identification Based on a 2-D Representation

Music Instrument Identification Based on a 2-D Representatio...

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International Conference on Electrical, Electronics, Communication, computer and Optimization Techniques (ICEECCOT)

作者： Alekhya Ghosh Arghadeep Pal Dibakar Sil Sarbani Palit Electronics and Communication Engineering IRPEL University of Calcutta Kolkata India Electronics and Communication Engineering National Institute of Technology Durgapur India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9781538651308

ISBN: (纸本)9781538651315

Automatic recognition of musical instruments has been a great problem in the field of digital signal processing. In this work a novel, compact set of features obtained by transforming a music signal to the spatial domain is introduced with promising results. Preliminary results on a popular music database, presented in terms of precision, recall and identification accuracy, are highly encouraging. The best result comprises an accuracy of 84.02% by Decision Tree (DT) for a set of 9 instruments belonging to different families. The accuracy for predicting the instrument family 96.07% for string family and for wind instrument the overall prediction accuracy is 90.78%. Further inclusion of some other features from some available works are also checked leading to slight increase in the accuracy. The obtained results are also compared with the result predicted by the VGG - 16 network on the same dataset. The feature set containing 8 features that introduces less computational complexity compared to available works, may be considered as a major contribution. The comparison shows, we can replicate nearly accurate classification through our proposed method in less amount of time than deep neural architecture.

关键词： Instruments Decision trees Forestry Feature extraction Timbre Matrix converters

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Text Independent Writer Identification for Telugu Script Using Directional Filter Based Features

Text Independent Writer Identification for Telugu Script Usi...

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International Conference on Document Analysis and recognition

作者： Chris Andrew Santhoshini Reddy Viswanath Pulabaigari Umapada Pal International Institute of Information Technology Bangalore Karnataka IN Indian Institute of Information Technology Sri City Andhra Pradesh India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata West Bengal IN

In this paper, a novel approach for writer identification from handwritten Telugu script is introduced. A new dataset of Telugu handwritten documents of 150 writers called the IIITS-THDB is created under this work for doing the experimental analysis and this dataset is made available publicly. Descriptive convolution based features are extracted from given handwritten text using directional filters. Feature reduction is performed to find the optimal linear subspace for the extracted features and identification is done using these reduced features. Smaller blocks of text with uniform textures are created using the available data that show an improved identification accuracy during experimentation. We also study the effects of noise and compare the results of our features with various other features over our dataset.

关键词： Feature extraction Kernel Convolution Writing Junctions Zirconium Image edge detection

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Synthetic data generation for Indic handwritten text recognition

arXiv

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arXiv 2018年

作者： Roy, Partha Pratim Mohta, Akash Chaudhuri, Bidyut B. Dept. of CSE Indian Institute of Technology Roorkee India Dept. of ECE Institute of Engineering and Management Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper presents a novel approach to generate synthetic dataset for handwritten word recognition systems. It is difficult to recognize handwritten scripts for which sufficient training data is not readily available or it may be expensive to collect such data. Hence, it becomes hard to train recognition systems owing to lack of proper dataset. To overcome such problems, synthetic data could be used to create or expand the existing training dataset to improve recognition performance. Any available digital data from online newspaper and such sources can be used to generate synthetic data. In this paper, we propose to add distortion/deformation to digital data in such a way that the underlying pattern is preserved, so that the image so produced bears a close similarity to actual handwritten samples. The images thus produced can be used independently to train the system or be combined with natural handwritten data to augment the original dataset and improve the recognition system. We experimented using synthetic data to improve the recognition accuracy of isolated characters and words. The framework is tested on 2 Indic scripts - Devanagari (Hindi) and Bengali (Bangla), for numeral, character and word recognition. We have obtained encouraging results from the experiment. Finally, the experiment with Latin text verifies the utility of the approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Hidden Markov models

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Tamil Handwritten City Name Database Development and recognition for Postal Automation

Tamil Handwritten City Name Database Development and Recogni...

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International Conference on Document Analysis and recognition

作者： S. Thadchanamoorthy N.D. Kodikara H.L. Premaretne Umapada Pal Fumitaka Kimura Eastern University Sri Lanka School of Computing University of Colombo Sri Lanka Computer Vision and Pattern Recognition unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9781479901937

Although there are some reports on offline Tamil isolated handwritten character recognition, to our knowledge there is only two reports on Tamil off-line handwritten word recognition. Also no city name dataset is available for Tamil script. In this paper we present a Tamil offline city name dataset, we developed, and propose a scheme for recognition. Because of the different writing style of various individuals, some of the characters in a Tamil city name may touch and accurate segmentation of such touching into individual characters is a difficult task. Avoiding proper segmentation here, we consider a city name string as a word and the recognition problem is treated as lexicon driven word recognition. In the proposed method, binarized city names are pre-segmented into primitives (individual character or its parts). Primitive components of each city name are then merged into possible characters to get the best city name using dynamic programming. For merging, total likelihood of characters is used as the objective function and character likelihood is computed based on Modified Quadratic Discriminant Function (MQDF), where direction features are applied. A dataset of 265 Tamil city names is developed. and the database will be available freely to the researchers. From the experiment of the proposed scheme 96.89% city name accuracy is obtained from this dataset.

关键词： Cities and towns Handwriting recognition Accuracy Image segmentation Dynamic programming Feature extraction Cavity resonators

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A method to generate synthetically warped document image

arXiv

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arXiv 2019年

作者： Garai, Arpan Biswas, Samit Mandal, Sekhar Chaudhuri, Bidyut B. Department of Computer Science and Technology Indian Institute of Engineering Sciences and Technology Shibpur Hawrah West Bengal711103 India Techno India University Kolkata 3 Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

The digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarping of the document suffers due to the limited availability of benchmark public dataset. In recent times, deep learning based approaches are used to solve the problems accurately. To train most of the deep neural networks a large number of document images is required and generating such a large volume of document images manually is difficult. In this paper, we propose a technique to generate a synthetic warped image from a flat-bedded scanned document image. It is done by calculating warping factors for each pixel position using two warping position parameters (WPP) and eight warping control parameters (WCP). These parameters can be specified as needed depending upon the desired warping. The results are compared with similar real captured images both qualitative and quantitative way. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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