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CNN based common approach to handwritten character recognition of multiple scripts 13

CNN based common approach to handwritten character recogniti...

引用

13th International Conference on Document Analysis and recognition, ICDAR 2015

作者： Maitra, Durjoy Sen Bhattacharya, Ujjwal Parui, Swapan K. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata-108 India

ISBN: (纸本)9781479918058

There are many scripts in the world, several of which are used by hundreds of millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. However, convolutional neural network (CNN) has recently been used as an efficient unsupervised feature vector extractor. Although such a network can be used as a unified framework for both feature extraction and classification, it is more efficient as a feature extractor than as a classifier. In the present study, we performed certain amount of training of a 5-layer CNN for a moderately large class character recognition problem. We used this CNN trained for a larger class recognition problem towards feature extraction of samples of several smaller class recognition problems. In each case, a distinct Support Vector Machine (SVM) was used as the corresponding classifier. In particular, the CNN of the present study is trained using samples of a standard 50-class Bangla basic character database and features have been extracted for 5 different 10-class numeral recognition problems of English, Devanagari, Bangla, Telugu and Oriya each of which is an official Indian script. recognition accuracies are comparable with the state-of-the-art. © 2015 IEEE.

关键词： Character recognition

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New Sharpness Features for Image Type Classification Based on Textual Information

New Sharpness Features for Image Type Classification Based o...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： K. S. Raghunandan Palaiahnakote Shivakumara G. Hemantha Kumar Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Narional Key Lab for Novel Software Technology Nanjing University Nanjing China

Achieving good recognition results from a single method for text lines in video/natural scene images captured by high resolution cameras or low resolution mobile cameras, and images in web pages, is often hard. In this paper, we propose new sharpness based features of textual portion of each input text line image using HSI color space for the classification of an input image into one of the four classes (video, scene, mobile or born digital). This helps in choosing an appropriate method based on the class type of the input text for its improved recognition rate. For a given input text line image, the proposed method obtains H, S and I images. Then Canny edge images are obtained for H, S and I spaces, which results in text candidates. We perform sliding window operation over the text candidate image of each text line of each color space to estimate new sharpness by calculating stroke width and gradient information. The sharpness values of the text lines of the three color spaces are then fed to k-means clustering with maximum, minimum and average guesses, which results in three respective clusters. The mean of each cluster for respective color spaces outputs a feature vector having nine feature values for image classification with the help of an SVM classifier. Experimental results on standard datasets, namely, ICDAR 2013, ICDAR 2015 video, ICDAR 2015 natural scene data, ICDAR 2013 born digital data and the images captured by a mobile camera (our own data) show that the proposed classification method helps in improving recognition results.

关键词： Mobile communication Text recognition Image edge detection Image resolution Image color analysis Digital images Optical character recognition software

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SSRBC 2016: Sclera Segmentation and recognition Benchmarking Competition

SSRBC 2016: Sclera Segmentation and Recognition Benchmarking...

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IAPR International Conference on Biometrics (ICB)

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

ISBN: (纸本)9781509018703

This article reports and summarizes the results of a competition on sclera segmentation and recognition benchmarking, called Sclera Segmentation and recognition Benchmarking Competition 2016 (SSRBC 2016). It was organized in the context of the 9th IAPR International Conference on Biometrics (ICB 2016). The goal of this competition was to record the recent developments in sclera segmentation and recognition, and also to gain the attention of researchers on this subject of biometrics. In this regard, we have used a multi-angle sclera dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82*2) different eyes. We have prepared a manual segmentation mask of these images to create the baseline for both tasks. We have, furthermore, adopted precision and recall based statistical measures to evaluate the effectiveness of the segmentation and the ranks of the competing algorithms. The recognition accuracy measure has been employed to measure the recognition task. To summarize, twelve participants registered for the competition, and among them, three participants submitted their algorithms/ systems for the segmentation task and two their recognition algorithm. The results produced by these algorithms reflect developments in the literature of sclera segmentation and recognition, employing cutting edge segmentation techniques. Along with the algorithms of three competing teams and their results, the MASD version 1 dataset will also be freely available for research purposes from the organizer's website. The competition also demonstrates the recent interests of researchers from academia as well as industry on this subject of biometrics.

关键词： Image segmentation Clustering algorithms Iris recognition Manuals Feature extraction

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Fourier Coefficients for Fraud Handwritten Document Classification through Age Analysis

Fourier Coefficients for Fraud Handwritten Document Classifi...

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International Workshop on Frontiers in Handwriting recognition

作者： K.S. Raghunandan Palaiahnakote Shivakumara B.J. Navya G. Pooja Navya Prakash G. Hemantha Kumar Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer Science 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

ISBN: (纸本)9781509009824

As new digital technologies emerge to improve living style, at the same time, it also lead to increase crimes. Unlike existing approaches that use content of handwriting for fraud/forged document identification, in this paper we propose a novel approach that explores the quality of handwritten documents by considering both foreground and background information to identify whether it is old or new. The proposed approach works based on the fact that if a fraud document is created with some gaps after the original one, the fraud document happened to be a new one and the original happened to be an old one in this work. To identify whether a given handwritten document is old or new with gaps, we propose to divide Fourier coefficients of the input image into positive and negative coefficient images, and then reconstruct respective images to conquer two reconstructed ones. The contrast of the reconstructed images obtained before and after divide-conquer is studied to analyze the ages of the document based on image quality. The proposed approach finds a unique relationship between reconstructed images, obtained before and after divide-conquer, to identify the input image as old or new. To evaluate the proposed approach, we conduct experiments on our own handwritten dataset and a standard database, namely, Google-LIFE magazine. Comparative studies with the existing approaches show that the proposed approach outperforms the existing approaches in terms of classification rate.

关键词： Image reconstruction Image edge detection Ink Printers Forgery Distortion Feature extraction

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Fast and efficent multimodal eye biometrics using projective dictionary pair learning

Fast and efficent multimodal eye biometrics using projective...

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Congress on Evolutionary Computation

作者： Abhijit Das Prabir Mondal Umapada Pal Miguel Angel Ferrer Michael Blumenstein Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain School of Software University of Technology Sydney Australia

ISBN: (纸本)9781509006243

This work proposes a projective pairwise dictionary learning-based approach for fast and efficient multimodal eye biometrics. The work uses a faster Projective pairwise Discriminative Dictionary Learning (DL) in contrast to the traditional DL which uses synthesis DL. Projective Pairwise Discriminative Dictionary (PPDD) uses a synthesis dictionary and an analysis dictionary jointly to achieve the goal of pattern representation and discrimination. As the PPDD process of DL is in contrast to the use of l 0 or l 1 -norm sparsity constraints on the representation coefficients adopted in most traditional DL, it works faster than other DL. Moreover, the blending of synthesis dictionary and an analysis dictionary also enhance the feature representation of the complex eye patterns. We employed the combination of sclera and iris traits to establish multimodal biometrics. The experimental study and analysis conducted fulfill the hypothesis we considered. In this work we employed a part of the UBIRIS version 1 dataset to conduct the experiments.

关键词： Dictionaries Iris recognition Training Time complexity pattern recognition

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Offline handwritten Devanagari word recognition: Information fusion at feature and classifier levels

Offline handwritten Devanagari word recognition: Information...

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Asian Conference on pattern recognition (ACPR)

作者： Bikash Shaw Ujjwal Bhattacharya Swapan Kumar Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata

ISBN: (纸本)9781479961016

This article presents our recent study on fusion of information at feature and classifier output levels for improved performance of offline handwritten Devanagari word recognition. We consider here two state-of-the-art features, viz., Directional Distance Distribution (DDD) and Gradient-Structural-Concavity (GSC) features along with multi-class SVM classifiers. Here, we study various combinations of DDD features along with one or more features from the GSC feature set. We experiment by presenting different combined feature vectors as input to SVM classifiers. Also, the output vectors of different SVM classifiers fed with different feature vectors are combined by another SVM classifier. The combination of the outputs of two SVMs each being fed with a different feature vector provides superior performance to the performance of a single SVM classifier fed with the combined feature vector. Experimental results are obtained on a large handwritten Devanagari word sample image database of 100 Indian town names. The recognition results on its test samples show that SVM recognition output of DDD features combined with the SVM output of GSC features improves the final recognition accuracy significantly.

关键词： Handwriting recognition Support vector machines Feature extraction Shape Databases Automation

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Writer Identification from offline isolated Bangla characters and numerals

Writer Identification from offline isolated Bangla character...

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International Conference on Document Analysis and recognition

作者： Chandranath Adak Bidyut B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Writer identification is an essential component in computational forensic. In this paper, we attempt to do this job based only on isolated characters and numerals. For that, at first, some points of interest (keypoints) on the image are detected by structural analysis and SIFT based detector. Then we calculate a set of features within a certain neighborhood of the keypoint and employ fusion rule on multiple probabilistic SVM classifiers output for writer identification. For experimental analysis, a database containing 212,300 isolated Bangla orthosyllabic characters and numerals are generated with the help of 100 writers. We obtain fairly good result to identify a writer. We also try to find a small set of highly discriminative characters storing extra information about the writing style of an individual.

关键词： Chlorine

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New texture-spatial features for keyword spotting in video images 3

New texture-spatial features for keyword spotting in video i...

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3rd IAPR Asian Conference on pattern recognition, ACPR 2015

作者： Shivakumara, Palaiahnakote Liang, Guozhu Roy, Sangheeta Pal, Umapada Lu, Tong Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479961009

Keyword spotting in video document images is challenging due to low resolution and complex background of video images. We propose the combination of Texture-Spatial-Features (TSF) for keyword spotting in video images without recognizing them. First, a segmentation method extracts words from text lines in each video image. Then we propose the set of texture features for identifying text candidates in the word image with the help of k-means clustering. The proposed method finds proximity between text candidates to study the spatial arrangement of pixels that result in feature vectors for spotting words in the input frame. The proposed method is evaluated on word images of different fonts, contrasts, backgrounds and font sizes, which are chosen from standard databases such as ICDAR 2013 video and our video data. Experimental results show that the proposed method outperforms the existing method in terms of recall, precision and f-measure. © 2015 IEEE.

关键词： Image segmentation

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CNN based common approach to handwritten character recognition of multiple scripts

CNN based common approach to handwritten character recogniti...

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International Conference on Document Analysis and recognition

作者： Durjoy Sen Maitra Ujjwal Bhattacharya Swapan K. Parui Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India

ISBN: (纸本)9781479918065

关键词： Handwriting recognition Databases Accuracy Training

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An efficient line segmentation approach for handwritten Bangla document image

An efficient line segmentation approach for handwritten Bang...

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International Conference on Advances in pattern recognition (ICAPR)

作者： K. Mullick S. Banerjee U. Bhattacharya Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

Text line segmentation plays a vital role in the overall performance of a document recognition system. In the literature, similar segmentation works for offline handwritten Bangla documents are rarely found. On the other hand, certain peculiarities of handwritten Bangla script such as widespread occurrences of ascenders and descenders or some of its characters appearing only as an ascender or descender often cause unique difficulties to this segmentation task. Existence of connected components over a number of successive text lines is a common phenomenon in unconstrained handwritten Bangla documents. In this article, we propose a novel and efficient approach for text line segmentation where initially, we smudge the input document image, to blur-out white spaces between words, while preserving gaps between consecutive lines. Next, we obtain an initial segmentation scheme by shredding the image based on the white most pixels in between consecutive smudged lines. Multi-line connected components have been taken care of by thinning, and then finding the most probable point of separation in the component. Combining it with the initial segmentation, we obtain the final output. The proposed approach has been evaluated on ICDAR 2013 Handwriting Segmentation Contest dataset of Bangla. The segmentation results show the efficiency of the proposed approach.

关键词： Image segmentation Junctions Image color analysis Estimation Transforms Handwriting recognition Frequency modulation

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