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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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Date field extraction in handwritten documents

Date field extraction in handwritten documents

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International Conference on pattern recognition

作者： Ranju Mandal Partha Pratim Roy Umapada Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Laboratoire dInformatique Université François Rabelais Tours France

Automatic extraction of date patterns from handwritten document involves difficult challenges due to writing styles of different individuals, touching characters and confusion among identification of alphabets and digits. In this paper, we propose a framework for retrieval of date patterns from handwritten documents. The method first classifies word components of each text line into month and non-month class using word level feature. Next, non-month words are segmented into individual components and classified into one of alphabet, digit or punctuation. Using this information of word and character level components, the date patterns are searched first using voting approach and then we detect the candidate lines for numeric and semi-numeric date using regular expression. Gradient based features and Support Vector Machine (SVM) are used in our work for classification. The experiment is performed on handwritten dataset and we have obtained encouraging results from it.

关键词： Support vector machines Feature extraction Handwriting recognition pattern matching Character recognition Image segmentation

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Hindi Off-Line Signature Verification

Hindi Off-Line Signature Verification

引用

International Workshop on Frontiers in Handwriting recognition

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

Handwritten Signatures are one of the widely used biometrics for document authentication as well as human authorization. The purpose of this paper is to present an off-line signature verification system involving Hindi signatures. Signature verification is a process by which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite of substantial research in the field of signature verification involving Western signatures, very little attention has been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, Persian etc. In this paper, the performance of an off-line signature verification system involving Hindi signatures, whose style is distinct from Western scripts, has been investigated. The gradient and Zernike moment features were employed and Support Vector Machines (SVMs) were considered for verification. To the best of the authors' knowledge, Hindi signatures have never been used for the task of signature verification and this is the first report of using Hindi signatures in this area. The Hindi signature database employed for experimentation consisted of 840 (35×24) genuine signatures and 1050 (35×30) forgeries. An encouraging accuracy of 7.42% FRR and 4.28% FAR were obtained following experimentation when the gradient features were employed.

关键词： Feature extraction Forgery Databases Support vector machines Training Testing

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Off-Line Bangla Signature Verification

Off-Line Bangla Signature Verification

引用

IAPR International Workshop on Document Analysis Systems, DAS

作者： Srikanta Pal Vu Nguyen Michael Blumenstein Umapada Pal School of Information and Communication Technology Griffith University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In the field of information security, biometric systems play an important role. Within biometrics, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an individual authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite substantial research in the field of signature verification involving Western signatures, very few works have been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, or Persian etc. In this paper, the performance of an off-line signature verification system involving Bangla signatures, whose style is distinct from Western scripts, was investigated. The Gaussian Grid feature extraction technique was employed for feature extraction and Support Vector Machines (SVMs) were considered for classification. The Bangla signature database employed in the experiments consisted of 3000 forgeries and 2400 genuine signatures. An encouraging accuracy of 90.4% was obtained from the experiments.

关键词： Forgery Feature extraction Support vector machines Databases Training Educational institutions

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A New Method for Word Segmentation from Arbitrarily-Oriented Video Text Lines

A New Method for Word Segmentation from Arbitrarily-Oriented...

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

作者： Nabin Sharma Palaiahnakote Shivakumara Umapada Pal Michael Blumenstein Chew Lim Tan Griffith University Queensland Australia School of Computing National University of Singapore Singapore Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Word segmentation has become a research topic to improve OCR accuracy for video text recognition, because a video text line suffers from arbitrary orientation, complex background and low resolution. Therefore, for word segmentation from arbitrarily-oriented video text lines, in this paper, we extract four new gradient directional features for each Canny edge pixel of the input text line image to produce four respective pixel candidate images. The union of four pixel candidate images is performed to obtain a text candidate image. The sequence of the components in the text candidate image according to the text line is determined using nearest neighbor criteria. Then we propose a two-stage method for segmenting words. In the first stage, for the distances between the components, we apply K-means clustering with K=2 to get probable word and non-word spacing clusters. The words are segmented based on probable word spacing and all other components are passed to the second stage for segmenting correct words. For each segmented and un-segmented words passed to the second stage, the method repeats all the steps until the K-means clustering step to find probable word and non-word spacing clusters. Then the method considers cluster nature, height and width of the components to identify the correct word spacing. The method is tested extensively on video curved text lines, non-horizontal straight lines, horizontal straight lines and text lines from the ICDAR-2003 competition data. Experimental results and a comparative study shows the results are encouraging and promising.

关键词： Image segmentation Image edge detection Feature extraction Text recognition Optical character recognition software Vectors Image resolution

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A New Method for Arbitrarily-Oriented Text Detection in Video

A New Method for Arbitrarily-Oriented Text Detection in Vide...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Nabin Sharma Palaiahnakote Shivakumara Umapada Pal Michael Blumenstein Chew Lim Tan Griffith University QLD Australia School of Computing National University of Singapore Singapore Computer Vision and Pattern Recognition Unit Indian Statistical Unit Kolkata India

Text detection in video frames plays a vital role in enhancing the performance of information extraction systems because the text in video frames helps in indexing and retrieving video efficiently and accurately. This paper presents a new method for arbitrarily-oriented text detection in video, based on dominant text pixel selection, text representatives and region growing. The method uses gradient pixel direction and magnitude corresponding to Sobel edge pixels of the input frame to obtain dominant text pixels. Edge components in the Sobel edge map corresponding to dominant text pixels are then extracted and we call them text representatives. We eliminate broken segments of each text representatives to get candidate text representatives. Then the perimeter of candidate text representatives grows along the text direction in the Sobel edge map to group the neighboring text components which we call word patches. The word patches are used for finding the direction of text lines and then the word patches are expanded in the same direction in the Sobel edge map to group the neighboring word patches and to restore missing text information. This results in extraction of arbitrarily-oriented text from the video frame. To evaluate the method, we considered arbitrarily-oriented data, non-horizontal data, horizontal data, Hua's data and ICDAR-2003 competition data (Camera images). The experimental results show that the proposed method outperforms the existing method in terms of recall and f-measure.

关键词： Cameras Image edge detection Feature extraction Classification algorithms Image resolution Educational institutions pattern recognition

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Combination of product graph and random walk kernel for symbol spotting in graphical documents

Combination of product graph and random walk kernel for symb...

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International Conference on pattern recognition

作者： Anjan Dutta Jaume Gibert Josep Lladós Horst Bunke Umapada Pal Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Institute of Computer Science and Applied Mathematics Universitat Bern Bern Switzerland Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781467322164

This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging.

关键词： Kernel Computational modeling pattern recognition Databases Labeling Equations Performance evaluation

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Multi-lingual City Name recognition for indian Postal Automation

Multi-lingual City Name Recognition for Indian Postal Automa...

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International Workshop on Frontiers in Handwriting recognition

作者： Umapada Pal Ramit Kumar Roy Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Saint Xaviers College Kolkata India Graduate School of Engineering Mie University Tsu Japan

Under three-language formula, the destination address block of postal document of an indian state is generally written in three languages: English, Hindi and the State official language. From the statistical analysis we found that 12.37%, 76.32% and 10.21% postal documents are written in Bangla, English and Devanagari script, respectively. Because of inter-mixing of these scripts in postal address writings, it is very difficult to identify the script by which a city name is written. To avoid such script identification difficulties, in this paper we proposed a lexicon-driven method for multi-lingual (English, Hindi and Bangla) city name recognition for indian postal automation. In the proposed scheme, at first, to take care of slanted handwriting of different individuals a slant correction technique is performed. Next, a water reservoir concept is applied to pre-segment the slant corrected city names into possible primitive components (characters or its parts). Pre-segmented components of a city name are then merged into possible characters to get the best city name using the lexicon information. In order to merge these primitive components into characters and to find optimum character segmentation, dynamic programming (DP) is applied using total likelihood of the characters of a city name as an objective function. We tested our system on 16132 indian trilingual city names and 92.25% overall recognition accuracy was obtained.

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

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Lexicon Reduction Technique for Bangla Handwritten Word recognition

Lexicon Reduction Technique for Bangla Handwritten Word Reco...

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IAPR International Workshop on Document Analysis Systems, DAS

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

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.

关键词： Handwriting recognition Hidden Markov models Shape Vectors Feature extraction Conferences

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Cursive Handwritten Segmentation and recognition for Instructional Videos

Cursive Handwritten Segmentation and Recognition for Instruc...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Ali Shariq Imran Sukalpa Chanda Faouzi Alaya Cheikh Katrin Franke Umapada Pal Dept. of Computer Science and Media Technology Gjϕvik University College Gjøvik Norway Hogskolen i Gjovik Gjovik Oppland NO Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, we address the issues pertaining to segmentation and recognition of cursive handwritten text from chalkboard lecture videos. Recognizing handwritten text is a challenging problem in instructor-led lecture video. The task gets even tougher with varying handwriting styles and blackboard type. Unlike handwritten text on whiteboard and electronic boards, chalkboard represents serious challenges such as, lack of uniform edge density, weak chalk contrast against blackboard and leftover chalk dust noise as a result of erasing -- and many others. Moreover, the varying color of boards and the illumination changes within the video makes it impossible to use trivial thresholding techniques, for the extraction of content. Many universities throughout the world still heavily rely on chalkboard as a mode of instruction. Therefore, recognizing these lecture content will not only aid in indexing and retrieval applications but will also help understand high level video semantics, useful for Multi-media Learning Objects (MLO). In order to encounter those adversaries, we here propose a system for segmentation and recognition of cursive handwritten text from chalkboard lecture videos. We first create a foreground model to segment background blackboard. We then segment the text characters using one-dimensional vertical histogram. Later, we extract gradient based features and classify those characters using an SVM classifier. We obtained an encouraging accuracy of 86.28% on 5-fold cross validation.

关键词： Videos Feature extraction Text recognition Support vector machines Databases Kernel Handwriting recognition

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