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National Conference on computer vision, pattern recognition, Image Processing and Graphics (NCVPRIPG)

作者： Dibyayan Chakraborty Partha Pratim Roy Jose M. Alvarez Umapada Pal Department of computer Science Ramakrishna Mission Vivekananda University Howrah India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India NICTA Australia

Book flipping scanning refers to the process of recording a book while the user performs the flipping action of its pages. In recent years it has gained much attention as it reduces the workload of book digitization significantly. It is a challenging task because flipping at random speed and direction causes difficulties to identify distinct open page images (OPI) which represent each page of the book. In this paper, we propose a fast technique for removing duplicate open pages introduced in the video stream due to erroneous flipping. We present an algorithm that exploits cues from edge information of flipping pages. The nature of the cues extracted from the region of interest (ROI) of the frame, determines the flipping or an open state of a page whereas temporal position a flipping page determines the direction of the flipping. Combining these information we decide whether an open page image is a duplicate or not. Experiments are performed on video documents recorded using a standard resolution camera to validate the duplicate open page removal algorithm and we have obtained 95% accuracy.

关键词： Streaming media Feature extraction Cameras Optical character recognition software Image edge detection Accuracy Performance evaluation

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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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Digital Imaging Computing: Techniques and Applications, DICTA 2005

作者： Roy, A. Bhowmik, T.K. Parui, S.K. Roy, U. Dept. of Computer and System Sciences Visva-Bharati Santiniketan 731235 India IBM Global Services Pvt. Ltd. Embassy Golf Link Bangalore - 560 071 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata-700 108 India

ISBN: (纸本)0769524672

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. © 2005 IEEE.

关键词： Image segmentation

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Machine Authentication of Security Documents

Machine Authentication of Security Documents

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International Conference on Document Analysis and recognition

作者： Utpal Garain Biswajit Halder Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of IT Greater Kolkata College of Engineering and Management Baruipur West Bengal India

ISBN: (纸本)9781424445004

This paper presents a pioneering effort towards machine authentication of security documents like bank cheques, legal deeds, certificates, etc. that fall under the same class as far as security is concerned. The proposed method first computationally extracts the security features from the document images and then the notion of dasiagenuinepsila vs. dasiaduplicatepsila is defined in the feature space. Bank cheques are taken as a reference for conducting the present experiment. Support Vector Machines (SVMs) and neural networks (NN) are involved to verify authenticity of these cheques. Results on a test dataset of 200 samples show that the proposed approach achieves about 98% accuracy for discriminating duplicate cheques from genuine ones. This strongly attests the viability of involving machine in authenticating security documents.

关键词： Authentication Security Printing Ink pattern recognition Feature extraction Support vector machines Neural networks Law Legal factors

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Detection of QRS complex of ECG signals from square-derivative curve

Modelling, Measurement and Control C

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Modelling, Measurement and Control C 2004年第1-2期65卷 15-27页

作者： Mitra, S. Mitra, M. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata 700108 India Department of Applied Physics University College of Science and Tech. University of Calcutta Kolkata 700009 India

An approach to QRS complex detection based on the selection of maximum area zone of square derivative curve is presented in this paper. At first the ECG signals are being extracted from paper ECG records by an automated data acquisition system which has been developed by using image processing techniques. Then the QRS complex of each ECG signal is detected even in the presence of power line interference and baseline drift to calculate the sampling period for further analysis in frequency plane for disease identification. A very high accuracy level (∼ 99.4%) has been achieved in detection of QRS complex by this method.

关键词： computer vision

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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 Fast Word Retrieval Technique Based on Kernelized Locality Sensitive Hashing

A Fast Word Retrieval Technique Based on Kernelized Locality...

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International Conference on Document Analysis and recognition

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

ISBN: (纸本)9781479901937

In this paper, we have presented a new and faster word retrieval approach, which is able to deal with heterogeneous document image collections. A certain amount of image features (statistical and Gabor Wavelet) are extracted, which inherently represent word's images. These features are used for generating hash table for fast retrieval of similar image from a very large image dataset. The decomposition and embedding of high-dimensional features and complex distance functions into a low-dimensional Hamming space helps to efficiently search items. However, existing methods do not apply for high-dimensional kernelized data when the underlying features' embedding for the kernel is unknown. The generalization of locality sensitive hashing (LSH) for arbitrary kernel is presented in the paper. The proposed algorithm provides sub-linear time similarity search and works for a wide class of similarity functions.

关键词： Feature extraction Kernel Databases Accuracy Vectors Training pattern recognition

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Text/Graphics Separation in Color Maps

Text/Graphics Separation in Color Maps

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International Conference on Computing: Theory and Applications, ICCTA

作者： Partha Pratim Roy Josep Llados Umapada Pal Computer Science Department Universitat Autònoma de Barcelona Barcelona Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India

Text/graphics separation in document image analysis is one of the main concerns in present research work. The complexity enhances when both text and graphics overlap in the context of maps in color images. This paper discusses a number of improvements to text/graphics separation methods to make it suitable for maps. Emphasize is given to the overlapping regions of text and graphics. It also discusses a method of color separation using clustering method for the purpose of text/graphics separation

关键词： computer graphics Image color analysis Image segmentation Histograms Bit error rate computer vision Text analysis Clustering methods Engineering drawings Testing

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A New Method for Handwritten Scene Text Detection in Video

A New Method for Handwritten Scene Text Detection in Video

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International Workshop on Frontiers in Handwriting recognition

作者： Palaiahnakote Shivakumara Anjan Dutta Umapada Pal Chew Lim Tan School of Computing National University of Singapore Singapore Computer Vision Center Universitat Authnòma de Barcelona Barcelona Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are many video frames where text line may be multi-oriented in nature. To the best of our knowledge there is no work on handwritten text detection in video, which is multi-oriented in nature. In this paper, we present a new method based on maximum color difference and boundary growing method for detection of multi-oriented handwritten scene text in video. The method computes maximum color difference for the average of R, G and B channels of the original frame to enhance the text information. The output of maximum color difference is fed to a K-means algorithm with K = 2 to separate text and non-text clusters. Text candidates are obtained by intersecting the text cluster with the Sobel output of the original frame. To tackle the fundamental problem of different orientations and skews of handwritten text, boundary growing method based on a nearest neighbor concept is employed. We evaluate the proposed method by testing on our own handwritten text database and publicly available video data (Hua's data). Experimental results obtained from the proposed method are promising.

关键词： Pixel Image edge detection Image color analysis Graphics Clustering algorithms Image resolution Measurement

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Gabor filter based hand-drawn underline removal in printed documents

Gabor filter based hand-drawn underline removal in printed d...

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International Conference on Automation, Control, Energy and Systems (ACES)

作者： Supriya Das Purnendu Banerjee Compute Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Society for Natural Language Technology Research Kolkata India

In this paper, a novel technique has been used for underline detection and removal in a Bengali and English document using Gabor filter and connected component analysis. In the underline detection module we first use Gabor filter in a specific direction to detect underline region and then use connected component analysis to detect particular underline. In second part i.e. underline removal module we use nearest neighbor approach. Our algorithm used for both touched and untouched underline. The experimental result of our proposed method demonstrates that it is able to improve Optical Character recognition (OCR) recognition accuracy.

关键词： Gabor filters Optical character recognition software Accuracy Noise Algorithm design and analysis Engines Filtering theory

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Local gradient difference features for classification of 2D-3D natural scene text images 25

Local gradient difference features for classification of 2D-...

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25th International Conference on pattern recognition, ICPR 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Raghavendra, Ramachandra Lu, Tong Pal, Umapada Lopresti, Daniel Anuar, Nor Badrul Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9781728188089

Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification. © 2020 IEEE

关键词： Image classification

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