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Attributed Paths for Layout-Based Document Retrieval 4th

Attributed Paths for Layout-Based Document Retrieval

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, icvgip 2018

作者： Sharma, Divya Harit, Gaurav Chattopadhyay, Chiranjoy Department of Computer Science and Engineering Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811393600

A document is rich in its layout. the entities of interest can be scattered over the document page. Traditional layout matching has involved modeling layout structure as grids, graphs, and spatial histograms of patches. In this paper we propose a new way of representing layout, which we call attributed paths. this representation admits a string edit distance based match measure. Our experiments show that layout based retrieval using attributed paths is computationally efficient and more effective. It also offers flexibility in tuning the match criterion. We have demonstrated effectiveness of attributed paths in performing layout based retrieval tasks on datasets of floor plan images [14] and journal pages [1]. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Information retrieval

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Symbol Spotting in Offline Handwritten Mathematical Expressions 4th

Symbol Spotting in Offline Handwritten Mathematical Expressi...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, icvgip 2018

作者： Aggarwal, Ridhi Harit, Gaurav Tiwari, Anil Kumar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur Jodhpur India Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811393600

Recognition of touching characters in mathematical expressions is a challenging problem in the field of document image analysis. Various approaches for recognizing touching maths symbols have been reported in literature, but they mainly dealt with printed expressions and handwritten numeral strings. In this work, a new segmentation-free approach is proposed which matches convex shape portions of symbols occurring in various layout such as subscript, superscript, fraction etc. and is able to perform spotting of symbols present in a handwritten expression. Our contribution lies in the design of a novel feature which can handle touching symbols effectively in the presence of handwriting variations. this recognition-based approach helps in spotting symbols in an expression even in the presence of clutter created by the presence of other symbols. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Character recognition

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MultiDIAS: A Hierarchical Multi-layered Document image Annotation System 4th

MultiDIAS: A Hierarchical Multi-layered Document Image Annot...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, icvgip 2018

作者： Poddar, Arnab Mukherjee, Rohan Mukhopadhyay, Jayanta Biswas, Prabir Kumar Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9789811393600

Content of the document images are often shows hierarchical multi-layered tree structure. Further, the algorithms for document image applications like line detection, paragraph detection, word recognition, layout analysis etc. require pixel level annotation. In this paper, a Multi-layered Document image Annotation System (MultiDIAS) has been introduced. the proposed system simultaneously provide a platform for hierarchical and pixel level annotation of document. MultiDIAS label the document image in four hierarchical layers (layout type, entity type, line type, word type) assigned by the user. the output generated are four ground-truth images and an XML file representing the metadata information. the MultiDIAS is tested on a complex handwritten manuscript written by renowned film director Satyajit Ray for the movie ‘Goopi Gyne Bagha Byne’. this annotated data generated using MultiDIAS can further be used in a wide range of applications of document image understanding and analysis. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image annotation

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An Efficient Character Segmentation Algorithm for Connected Handwritten Documents 4th

An Efficient Character Segmentation Algorithm for Connected ...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, icvgip 2018

作者： Rajput, Vishal Jayanthi, N. Indu, S. Department of Electronics and Communication Engineering PDPM-Indian Institute of Information Technology Design and Manufacturing Jabalpur JabalpurMP482005 India Department of Electronics and Communication Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789811393600

this paper proposes an efficient method of character segmentation for handwritten text. the main challenge in character segmentation of hand-written text is the varied size of each letter in different documents, connected alphabets in a word in cursive writing and the presence of ligatures within an open character. Hence, this paper proposes an adaptive vertical pixel count algorithm to solve the problem of over-segmentation due to the presence of open characters such as ‘w’, ‘v’ and ‘m’. Proposed algorithm works effectively against both the handwritten and standard text. the proposed method is evaluated on IAM and self-created data set. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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An Efficient Multi Lingual Optical Character Recognition System for indian Languages through Use of Bharati Script 4th

An Efficient Multi Lingual Optical Character Recognition Sys...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, icvgip 2018

作者： Vorugunti, Chandra Sekhar Chakravarthy, Srinivasa Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India Indian Institute of Technology-Madras Chennai India

ISBN: (纸本)9789811393600

Optical character recognition performs a critical part in interpreting videos and documents. Document specific issues like low image quality, distortions, composite background, noise etc. and language specific issues like cursive connectivity among the characters etc. makes OCR challenging and erroneous for indian languages. the language specific challenges can be overcome by computing the script-based features and can achieve better accuracy. Computing the script based invariant features and patterns is computationally complex and error prone. In this background, we put forward Bharathi script (*** ) based OCR system in which the inherent drawbacks of indian scripts i.e. Hindi, Tamil, Telugu etc. are eliminated. the proposed OCR model has been tested on a synthetic dataset of documents of Bharathi script (in which Hindi scripts are converted to Bharathi script). thorough experimental analysis with varied levels of noise confirms the promising results of character recognition accuracy of the proposed OCR model which out-performs the state-of-the-art OCR systems for indian scripts. the proposed model achieves 76.70% with test documents consists of 50% noise and 99.98% with test documents of 0% noise. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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A Deep Learning Architecture Based Dimensionality Reduction and Online Signature Verification 4th

A Deep Learning Architecture Based Dimensionality Reduction ...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, icvgip 2018

作者： Vorugunti, Chandra Sekhar Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India

ISBN: (纸本)9789811393600

In this paper, we propose a novel hybrid deep learning based autoencoder-CNN-Softmax architecture aims at obtaining reduced dimension feature set from raw feature set. the reduced feature set forms an input to CNN layers to learn deep global features. these global features are used to train the SoftMax layer for online signature classification. Ability to reduce the noisy features and to discover the hidden corelated features makes the proposed architecture light weight and efficient to use in critical applications like online signature verification (OSV) and to deploy in resource constraint mobile devices. We demonstrate the superiority of our model for feature correlation learning and signature classification by conducting experiments on standard datasets MCYT, SUSIG. the experimentation confirms that the proposed model achieves better accuracy (lower error rates) with a lesser number of features compared to the current state-of-the-art models. the proposed models yield state-of-the-art performance of 0.4% EER on MCYT-100 dataset and 3.47% with SUSIG dataset. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Classification (of information)

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Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion 4th

Word-Wise Handwriting Based Gender Identification Using Mult...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, icvgip 2018

作者： Asadzadeh Kaljahi, Maryam Vidya Varshini, P.V. Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Guru, D.S. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Vellore Institute of Technology VelloreTamil Nadu India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Studies in Computer Science Manasagangotri University of Mysuru Mysore India

ISBN: (纸本)9789811393600

Handwriting based gender identification at the word level is challenging due to free style writing, use of different scripts, and inadequate information. this paper presents a new method based on Multi-Gabor Response (MGR) fusion for gender identification at the word level. It first explores weighted-gradient features for word segmentation from text line images. For each word, the proposed method obtains eight Gabor response images. then it performs sliding window operation over MGR images to smooth the values. For each smoothed MGR images, we perform fusion operation that chooses the Gabor response value which contributes to the highest peak in the histogram. this process results in a feature matrix, which is fed to CNN for gender identification. Experimental results on our dataset (multi scripts) apart from English, and benchmark databases, namely, IAM, KHATT, and QUWI, which contain handwritten English and Arabic text, show that the proposed method outperforms the existing methods. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image segmentation

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Deep Learning Models for Medical image Analysis: Challenges and Future Directions 1

引用

7th International conference on Big Data analytics (BDA)

作者： Agrawal, R. K. Juneja, Akanksha Jawaharlal Nehru Univ New Delhi 110067 India

ISBN: (数字)9783030371883

ISBN: (纸本)9783030371883;9783030371876

Artificial neural network (ANN) introduced in the 1950s, is a machine learning framework inspired by the functioning of human neurons. However, for a long time the ANN remained inadequate in solving real problems, because of - the problems of overfitting and vanishing gradient while training a deep architecture, dearth of computation power, and non-availability of enough data for training the framework. this concept has lately re-emerged, in the form of Deep Learning (DL) which initially developed for computer vision and became immensely popular in several other domains. It gained traction in late 2012, when a DL approach i.e. convolutional neural network won in the imageNet Classification - an acclaimed worldwide computer vision competition. thereafter, researchers in practically every domain, including medical imaging, started vigorously contributing in the massively progressing field of DL. the success of DL methods can be owed to the availability of data, boosted computation power provided by the existing graphics processing units (GPUs), and ground-breaking training algorithms. In this paper, we have overviewed the area of DL in medical imaging, including (1) machine learning and DL basics, (2) cause of power of DL, (3) common DL models, (4) their applications to medical imaging and (5) challenges and future work in this field.

关键词： Medical image analysis Machine learning Deep Learning computer-aided diagnosis

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Benchmark Datasets for Offline Handwritten Gurmukhi Script Recognition 4th

Benchmark Datasets for Offline Handwritten Gurmukhi Script R...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, icvgip 2018

作者： Kumar, Munish Sharma, R.K. Jindal, M.K. Jindal, Simpel Rani Singh, Harjeet Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University BathindaPunjab India Computer Science and Engineering Department Thapar Institute of Engineering and Technology PatialaPunjab India Department of Computer Science and Applications Panjab University Regional Centre MuktsarPunjab India Department of Computer Science and Engineering Yadavindra College of Engineering Talwandi Sabo BathindaPunjab India

ISBN: (纸本)9789811393600

Handwritten character recognition is an imperative issue in the field of pattern recognition and machine learning research. In the recent years, several techniques for handwritten character recognition have been proposed. Due to the lack of publicly accessible benchmark datasets of Gurmukhi script, no extensive comparisons have been undertaken between those techniques, especially for this script. Over the years, datasets and benchmarks have proven their fundamental importance in character recognition research, and objective comparisons in many fields. this paper presents a collection of seven benchmark datasets (HWR-Gurmukhi_1.1, HWR-Gurmukhi_1.2, HWR-Gurmukhi_1.3, HWR-Gurmukhi_2.1, HWR-Gurmukhi_2.2, HWR-Gurmukhi_2.3, and HWR-Gurmukhi_3.1) with different sizes for offline handwritten Gurmukhi character recognition collected from various public places. A few exploratory outcomes based on precision, False Acceptance Rate (FAR), and False Rejection Rate (FRR) using different classification techniques, namely, k-NN, RBF-SVM, MLP, Neural Network, Decision Tree, and Random Forest are also presented in this paper. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Classification (of information)

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A Secure and Light Weight User Authentication System Based on Online Signature Verification for Resource Constrained Mobile Networks 4th

A Secure and Light Weight User Authentication System Based o...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, icvgip 2018

作者： Vorugunti, Chandra Sekhar Guru, D.S. Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India University of Mysore Mysore India

ISBN: (纸本)9789811393600

the rapid advances in mobile and networking technologies results in usage of mobiles for critical applications like m-commerce, m-payments etc. Even though mobile based services offer many benefits, authenticating the user logging into the system is a big challenge. To mitigate this concern, secure mobile applications based on user online signature verification (OSV) has been proposed. Unfortunately, these models would intensify the substantial computational overhead on thin and resource-constrained mobile devices. this summarizes for a critical need of OSV models which are computationally efficient and achieves higher classification accuracy. Recently, several OSV models have been defined in the literature. However, these models are not computationally effective for resource-constrained mobile devices, because the proposed verification models ought to require not only higher feature dimension but also heavy weight writer specific parameter fixation logic. In this manuscript, we propose an efficient and light weight OSV model for resource-constrained mobile devices. Our approach employs dimensionality reduction based on DBSCAN clustering technique and user specific parameter selection. thorough experimental analysis are conducted on benchmarking online signature datasets MCYT-100 (DB1) and MCYT-330 (DB2) datasets which confirms the efficiency of proposed model with latest OSV models. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Mobile security

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