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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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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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Evaluation of feature sensitivity to training data inaccuracy in detection of retinal lesions

Evaluation of feature sensitivity to training data inaccurac...

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Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Lauri Laaksonen Antti Hannuksela Ela Claridge Pauli Fält Markku Hauta-Kasari Hannu Uusitalo Lasse Lensu Machine Vision and Pattern Recognition Laboratory Lappeenranta university of Technology Lappeenranta Finland School of Computer Science The University of Birmingham United Kingdom School of Computing University of Eastern Finland Finland Department of Ophthalmology University of Tampere Finland TAUH Eye Center Tampere University Hospital Finland

computer aided diagnostic and segmentation tools have become increasingly important in reducing the workload of medical experts performing diagnosis, monitoring and documentation of various eye diseases such as age-related macular degeneration (AMD), diabetic retinopathy (DR) and glaucoma. Supervised methods have been developed for the segmentation and detection of lesions, and the reported performance has been good. The supervised methods, however, need representative data to properly train the classifier. Inaccuracies in the ground truth may have a significant impact on the performance of a supervised method as the training data are not representative. In this study, a quantitative evaluation of the sensitivity of different image features, including colour, texture, edge and higher-level features, to inaccuracy in the ground truth on exudates is presented. A mean decrease of approx. 20% in sensitivity and 13% in specificity was observed when using the most inaccurate training data.

关键词： Training data Sensitivity Retina Image color analysis Image segmentation Feature extraction Lesions

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Automatic extraction of correlation-entropy features for text document analysis directly in run-length compressed domain

Automatic extraction of correlation-entropy features for tex...

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International Conference on Document Analysis and recognition

作者： Mohammed Javed P. Nagabhushan B.B. Chaudhuri Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Automatic feature extraction plays a pivotal role in defining the overall performance of any Document Image Analysis system, which conventionally operates directly over uncompressed images, although most of the real time systems such as fax machines, digital libraries and e-governance applications accrue and archive the documents in the compressed form for the sake of storage and transfer efficiencies. However, this infers that the compressed documents need to be decompressed before carrying out any operation or analysis which warrants additional computing resources. This limitation in existing systems instigates motivation to explore for feature extraction techniques directly from the compressed documents and eventually design a document analysis system that works directly in compressed domain. Therefore, this research work proposes to extract novel correlation-entropy features directly from run-length compressed TIFF documents. Further, the research work also investigates different methods to demonstrate some of the straight forward application of the proposed features in carrying out compressed document image analysis such as text and non-text component detection, and subsequently performing compressed text line segmentation and characterization, all carried out in the compressed version of the printed text document without going through the stage of decompression. Finally, the experimental results reported validate the developed algorithms and also illustrate that the proposed features are quite powerful in distinguishing compressed text and non-text components.

关键词： Image coding Image segmentation

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A direct approach for word and character segmentation in run-length compressed documents with an application to word spotting

A direct approach for word and character segmentation in run...

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International Conference on Document Analysis and recognition

Segmentation of a text document into lines, words and characters is an important objective in application like OCR and related analytics. However in today's scenario, the documents are compressed for archival and transmission efficiency. Text segmentation in compressed documents warrants decompression, and needs additional computing resources. In this backdrop, the paper proposes a method for text segmentation directly in run-length compressed, printed English text documents. Line segmentation is done using the projection profile technique. Further segmentation into words and characters is accomplished by tracing the white runs along the base region of the text line. During the process, a run based region growing technique is applied in the spatial neighborhood of the white runs to trace the vertical space between the characters. After detecting the character spaces in the entire text line, the decision of word space and character space is made by computing the average character space. Subsequently based on the spatial position of the detected words and characters, their respective compressed segments are extracted. The proposed algorithm is tested with 1083 compressed text lines, and F-measure of 97.93% and 92.86% respectively for word and character segmentation are obtained. Finally an application of word spotting is also presented.

关键词： Optical character recognition software Image coding Adaptation models

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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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Sentiment analysis using cosine similarity measure

Sentiment analysis using cosine similarity measure

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International Conference on Recent Trends in Information Systems (ReTIS)

作者： Saprativa Bhattacharjee Anirban Das Ujjwal Bhattacharya Swapan K. Parui Sudipta Roy Department of Information Technology Sikkim Manipal Institute of Technology Sikkim India School of Computing Jimma University Jimma Ethiopia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Information Technology Assam University Silchar Assam India

The opinion of other people is often a major factor influencing our decisions. For a consumer it affects purchase decisions and for a producer or a service provider it helps in making business decisions. Companies spend a lot of money and time on surveys for gathering the public opinion on products and services. Now-a-days the web has become a hotspot for finding user opinions on almost anything under the sun. Both money and time can be saved by mining opinions from the web. Moreover, no survey can have a sample size, which can match that of the web. Each opinion generally expresses either positive, negative or neutral sentiment. The task of identifying these sentiments is called Sentiment Analysis. This work deals with the analysis of user sentiments in the Telecom domain. Since no such related standard database of users' opinions could be found, we developed one by mining the WWW. A major issue with these sample comments is that these are usually extremely noisy, containing numerous spelling and grammatical errors, acronyms, abbreviations, shortened or slang words etc. Such data cannot be used directly for analyzing sentiments. Hence, a lexicon based preprocessing algorithm is proposed for noise reduction. A novel idea based on Cosine Similarity measure is proposed for classifying the sentiment expressed by a user's comment into a five point scale of -2 (highly negative) to +2 (highly positive). The performance of the proposed strategy is compared with some of the well-known machine learning algorithms namely, Naive Bayes, Maximum Entropy and SVM. The proposed Cosine Similarity based classifier gives 82.09% accuracy for the 2-class problem of identifying positive and negative sentiments. It outperforms all other classifiers by a considerable margin in the 5-class sentiment classification problem with an accuracy of 71.5%. The same strategy is also used for categorizing each user comment into six different Telecom specific categories.

关键词： Accuracy Sentiment analysis Frequency conversion Dictionaries Support vector machines Training Telecommunications

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Pectoral muscle detection in digital breast tomosynthesis and mammography

Pectoral muscle detection in digital breast tomosynthesis an...

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3rd International MICCAI Workshop on Medical computer vision, MCV 2013

作者： Ghesu, Florin C. Wels, Michael Jerebko, Anna Sühling, Michael Hornegger, Joachim Kelm, B. Michael Siemens Corporate Technology Imaging and Computer Vision Erlangen Germany Pattern Recognition Department Friedrich Alexander University Erlangen Germany Siemens AG Healthcare Erlangen Germany

ISBN: (纸本)9783319055299

Screening and diagnosis of breast cancer with Digital Breast Tomosynthesis (DBT) and Mammography are increasingly supported by algorithms for automatic post-processing. The pectoral muscle, which dorsally delineates the breast tissue towards the chest wall, is an important anatomical structure for navigation. Along with the nipple and the skin, the pectoral muscle boundary is often used for reporting the location of breast lesions. It is visible in mediolateral oblique (MLO) views where it is well approximated by a straight line. Here, we propose two machine learning-based algorithms to robustly detect the pectoral muscle in MLO views from DBT and mammography. Embedded into the Marginal Space Learning framework, the algorithms involve the evaluation of multiple candidate boundaries in a hierarchical manner. To this end, we propose a novel method for candidate generation using a Hough-based approach. Experiments were performed on a set of 100 DBT volumes and 95 mammograms from different clinical cases. Our novel combined approach achieves competitive accuracy and robustness. In particular, for the DBT data, we achieve significantly lower deviation angle error and mean distance error than the standard approach. The proposed algorithms run within a few seconds. © 2014 Springer International Publishing Switzerland.

关键词： Learning algorithms

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A word association based approach for improving retrieval performance from noisy OCRed text 6

A word association based approach for improving retrieval pe...

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6th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2014

作者： Chakraborty, Anirban Ghosh, Kripabandhu Roy, Utpal Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata700108 India Department of Computer and System Sciences Siksha-Bhavana Visva-Bharati Santiniketan731235 India

ISBN: (纸本)9789897580482

OCR errors hurt retrieval performance to a great extent. Research has been done on modelling and correction of OCR errors. However, most of the existing systems use language dependent resources or training texts for studying the nature of errors. Not much research has been reported on improving retrieval performance from erroneous text when no training data is available. We propose an algorithm of detecting OCR errors and improving retrieval performance from the erroneous corpus. We present two versions of the algorithm: one based on word cooccurrence and the other based on Pointwise Mutual Information. Our algorithm does not use any training data or any language specific resources like thesaurus. It also does not use any knowledge about the language except that the word delimiter is a blank space. We have tested our algorithm on erroneous Bangla FIRE collection and obtained significant improvements. Copyright © 2014 SCITEPRESS - Science and Technology Publications All rights reserved.

关键词： Errors

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Entropy Computations of Document Images in Run-Length Compressed Domain

Entropy Computations of Document Images in Run-Length Compre...

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International Conference on Signal and Image Processing (ICSIP)

作者： P. Nagabhushan Mohammed Javed B.B. Chaudhuri Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Compression of documents, images, audios and videos have been traditionally practiced to increase the efficiency of data storage and transfer. However, in order to process or carry out any analytical computations, decompression has become an unavoidable pre-requisite. In this research work, we have attempted to compute the entropy, which is an important document analytic directly from the compressed documents. We use Conventional Entropy Quantifier (CEQ) and Spatial Entropy Quantifiers (SEQ) for entropy computations [1]. The entropies obtained are useful in applications like establishing equivalence, word spotting and document retrieval. Experiments have been performed with all the data sets of [1], at character, word and line levels taking compressed documents in run-length compressed domain. The algorithms developed are computational and space efficient, and results obtained match 100% with the results reported in [1].

关键词： Entropy Image coding Feature extraction Energy measurement Data mining Position measurement

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