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Bag-of-visual-words for signature-based multi-script document retrieval

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arXiv 2018年

作者： Mandal, Ranju Roy, Partha Pratim Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Griffith University QLD Australia Dept. of Computer Science & Engineering Indian Institute of Technology Roorkee India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Software University of Technology Sydney Australia

An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a Support Vector Machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e. signature strokes) and the background spatial information (i.e. background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The 'Tobacco' [1] document database and an indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents and promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Support vector machines

FWLBP: A scale invariant descriptor for texture classification

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arXiv 2018年

作者： Roy, Swalpa Kumar Bhattacharya, Nilavra Chanda, Bhabatosh Chaudhuri, Bidyut B. Ghosh, Dipak Kumar Optical Character Recognition Laboratory Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India School of Information University of Texas AustinTX78712 United States Image Processing Laboratory Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata700108 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Rourkela769008 India

In this paper we propose a novel texture descriptor called Fractal Weighted Local Binary pattern (FWLBP). The fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human viewpoint of surface roughness. We have utilized this property to construct a scale-invariant descriptor. Here, the input image is sampled using an augmented form of the local binary pattern (LBP) over three different radii, and then used an indexing operation to assign FD weights to the collected samples. The final histogram of the descriptor has its features calculated using LBP, and its weights computed from the FD image. The proposed descriptor is scale invariant, and is also robust in rotation or reflection, and partially tolerant to noise and illumination changes. In addition, the local fractal dimension is relatively insensitive to the bi-Lipschitz transformations, whereas its extension is adequate to precisely discriminate the fundamental of texture primitives. Experiment results carried out on standard texture databases show that the proposed descriptor achieved better classification rates compared to the state-of-the-art descriptors. Copyright © 2018, The Authors. All rights reserved.

关键词： Fractal dimension

Weighted-Gradient Features for Handwritten Line Segmentation

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Weighted-Gradient Features for Handwritten Line Segmentation

International Conference on pattern recognition

作者： Vijeta Khare Palaiahnakote Shivakumara B.J. Navya G.C. Swetha D. S. Guru Umapada Pal Tong Lu Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Studies in Computer Science University of Mysore Karnataka India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Text line segmentation from handwritten documents is challenging when a document image contains severe touching. In this paper, we propose a new idea based on Weighted-Gradient Features (WGF) for segmenting text lines. The proposed method finds the number of zero crossing points for every row of Canny edge image of the input one, which is considered as the weights of respective rows. The weights are then multiplied with gradient values of respective rows of the image to widen the gap between pixels in the middle portion of text and the other portions. Next, k-means clustering is performed on WGF to classify middle and other pixels of text. The method performs morphological operation to obtain word components as patches for the result of clustering. The patches in both the clusters are matched to find common patch areas, which helps in reducing touching effect. Then the proposed method checks linearity and non-linearity iteratively based on patch direction to segment text lines. The method is tested on our own and standard datasets, namely, Alaei, ICDAR 2013 robust competition on handwriting context and ICDAR 2015-HTR, to evaluate the performance. Further, the method is compared with the state of art methods to show its effectiveness and usefulness.

关键词： Image segmentation Image edge detection Linearity Writing Morphological operations Handwriting recognition Image restoration

Adaptive Multi-Gradient Kernels for Handwritting Based Gender Identification

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Adaptive Multi-Gradient Kernels for Handwritting Based Gende...

International Workshop on Frontiers in Handwriting recognition

作者： B. J Navya Palaiahnakote Shivakumara G.C Shwetha Sangheeta Roy D. S. Guru Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Handwriting based Gender identification is challenging due to unconstrained handwriting and individual differences in writing. To solve this problem, we propose a new adaptive multi-gradient of Sobel kernels for extracting Adaptive Multi-Gradient Features (AMGF). For extracted text lines, the proposed method finds dominant pixels based on directional symmetry of text pixels given by AMGF. We perform histogram operation for adaptive multi-gradient values extracted corresponding to dominant pixels. The gradient values that give the highest peak in respective histograms is chosen as features. This results in feature vector having four AMGF values. The same vector are generated for successive text lines in each image to study either consistency, which is expected for females or inconsistency, which is expected for males in writing styles. The correlation is estimated based on feature vectors of the first and the successive text lines until converging or diverging criteria is met. If convergence happens, the input document is considered as female else is considered as male. The method is tested on our own dataset, which includes large variations and standard datasets, namely, QUWI, IAM-1+IAM-2 and KHATT, to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms the existing methods.

关键词： Feature extraction Kernel Image edge detection Writing Histograms Junctions Laplace equations

Multi-Gradient Directional Features for Gender Identification

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Multi-Gradient Directional Features for Gender Identificatio...

International Conference on pattern recognition

作者： B.J. Navya G. C. Swetha Palaiahnakote Shivakumara Sangheeta Roy D. S. Guru Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Gender identification based on handwriting analysis has received a special attention to researchers in the field of document image analysis as it is useful for several real-time applications like forensic, population counting, etc. In this paper, we explore Multi-Gradient Directional (MGD) features, which provide direction of dominant pixels obtained by Canny edge image, and gradient direction symmetry. The proposed method further performs histogram operation for gradient angle information of dominant pixels of respective multi-gradient directional images to select angles, which contribute to the highest peak. This results in feature vectors. The process of feature vector formation continues for the segmented first, second, and third text lines in each image by male or female. Next, correlation is estimated for the vector of the first line with successive lines until converging or diverging criteria is met. If the convergence happens, a document is considered as by female, else is considered as by male. The method is tested on our own dataset, which includes images of different scripts, writers, papers, pens, and ages, and the standard database QUWI which includes Arabic and English texts, to demonstrate the efficiency of the proposed method. Comparative studies with the state of the art methods show that the proposed method is effective and useful.

关键词： Feature extraction Image edge detection Writing Junctions Histograms Face Support vector machines

A New RGB Based Fusion for Forged IMEI Number Detection in Mobile Images

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A New RGB Based Fusion for Forged IMEI Number Detection in M...

International Workshop on Frontiers in Handwriting recognition

作者： Palaiahnakote Shivakumara V. Basavaraja Harsha S. Gowda D. S. Guru Umapada Pal Tong Lu Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Studies in Computer Science University of Mysore Mysore Karnataka India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

As technology advances to make living comfortable for people, at the same time, different crimes also increase. One such sensitive crime is creating fake International Mobile Equipment Identity (IMEI) for smart mobile devices. In this paper, we present a new fusion based method using R, G and B color components for detecting forged IMEI numbers. To the best of our knowledge, this is the first work for forged IMEI number detection in mobile images. The proposed method first finds variances for R, G and B images of a forged input image to study local changes. The variances are used to derive weights for respective color components. The same weights are convolved with respective pixel values of R, G and B components, which results in the fused image. For the fused image, the proposed method extracts features based on sparsity, the number of connected components, and the average intensity values for edge components in respective R, G and B components, which gives six features. The proposed method finds absolute difference between fused and input images, which gives feature vector containing six difference values. The proposed method constructs templates based on samples chosen randomly. Feature vectors are compared with the templates for detecting forged IMEI numbers. Experiments are conducted on our own dataset and standard datasets to evaluate the proposed method. Furthermore, comparative studies with the related existing methods show that the proposed method outperforms the existing methods.

关键词： Feature extraction Image color analysis Image edge detection Forgery Software Printers Image segmentation

Sclera vessel pattern synthesis based on a non-parametric texture synthesis technique

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Sclera vessel pattern synthesis based on a non-parametric te...

International Conference on computer vision and Image Processing, CVIP 2016

作者： Das, Abhijit Mondal, Prabir Pal, Umapada Blumenstein, Michael Ferrer, Miguel A. Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9789811021060

This work proposes a sclera vessel texture pattern synthesis technique. Sclera texture was synthesized by a non-parametric based texture regeneration technique. A small number of classes from the UBIRIS version: 1 dataset was employed as primitive images. An appreciable result was achieved which solicits the successful synthesis of sclera texture patterns. It is difficult to get a huge collection real sclera data and hence such synthetic data will be useful to the researchers. © Springer Science+Business Media Singapore 2017.

关键词： Biometrics pattern Sclera Synthesis Texture

Indic handwritten script identification using offline-online multi-modal deep network

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arXiv 2018年

作者： Bhunia, Ayan Kumar Mukherjee, Subham Sain, Aneeshan Bhunia, Ankan Kumar Roy, Partha Pratim Pal, Umapada Institute for Media Innovation Nanyang Technological University Singapore Centre for Vision Speech and Signal Processing University of Surrey England United Kingdom Department of ECE Institute of Engineering & Management Kolkata India Department of EE Institute of Engineering & Management Kolkata India Department of Electrical Engineering Jadavpur University Department of CSE Indian Institute of Technology Roorkee India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, we propose a novel approach of word-level Indic script identification using only character-level data in training stage. Our method uses a multi-modal deep network which takes both offline and online modality of the data as input in order to explore the information from both the modalities jointly for script identification task. We take handwritten data in either modality as input and the opposite modality is generated through intermodality conversion. Thereafter, we feed this offline-online modality pair to our network. Hence, along with the advantage of utilizing information from both the modalities, the proposed framework can work for both offline and online script identification which alleviates the need for designing two separate script identification modules for individual modality. We also propose a novel conditional multi-modal fusion scheme to combine the information from offline and online modality which takes into account the original modality of the data being fed to our network and thus it combines adaptively. An exhaustive experimental study has been done on a data set including English(Roman) and 6 other official Indic scripts. Our proposed scheme outperforms traditional classifiers along with handcrafted features and deep learning based methods. Experiment results show that using only character level training data can achieve competitive performance against traditional training using word level data. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

A new cold feature based handwriting analysis for enthnicity/nationality identification

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arXiv 2018年

作者： Nag, Sauradip Shivakumara, Palaiahnakote Yirui, Wu Pal, Umapada Lu, Tong Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia College of Computer and Information Hohai University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, explores tangent angle for the contour pixels in each row and the mean of intensity values of each row in an image for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method. Copyright © 2018, The Authors. All rights reserved.

关键词： Crime