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arXiv 2022年

作者： Biswas, Sanket Banerjee, Ayan Lladós, Josep Pal, Umapada Computer Vision Center Computer Science Department Universitat Autònoma de Barcelona Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

Understanding documents with rich layouts is an essential step towards information extraction. Business intelligence processes often require the extraction of useful semantic content from documents at a large scale for subsequent decision-making tasks. In this context, instance-level segmentation of different document objects (title, sections, figures etc.) has emerged as an interesting problem for the document analysis and understanding community. To advance the research in this direction, we present a transformer-based model called DocSegTr for end-to-end instance segmentation of complex layouts in document images. The method adapts a twin attention module, for semantic reasoning, which helps to become highly computationally efficient compared with the state-of-the-art. To the best of our knowledge, this is the first work on transformer-based document segmentation. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ) and TableBank demonstrate that our model achieved comparable or better segmentation performance than the existing state-of-the-art approaches with the average precision of 89.4, 40.3, 83.4 and 93.3. This simple and flexible framework could serve as a promising baseline for instance-level recognition tasks in document images. © 2022, CC BY-SA.

关键词： Decision making

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SVM Based Scheme for Thai and English Script Identification

SVM Based Scheme for Thai and English Script Identification

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International Conference on Document Analysis and recognition

作者： S. Chanda O.R. Terrades U. Pal Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India Computer Vision Center Universitat Authnòma de Barcelona Barcelona Spain

In some Thai documents, a single text line of a document page may contain both Thai and English scripts. For the optical character recognition (OCR) of such a document page it is better to identify, at first, Thai and English script portions and then to use individual OCR system of the respective scripts on these identified portions. In this paper, a SVM based method is proposed for identification of word-wise printed English and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of the individual character group combining different character features obtained from structural shape, profile, component overlapping information, topological properties, water reservoir concept etc. Based on the experiment on 6110 data we obtained 99.36% script identification accuracy from the proposed scheme.

关键词： Support vector machines Optical character recognition software Support vector machine classification computer vision Optical network units Structural shapes Water resources Reservoirs Neural networks pattern recognition

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Segmentation-Free Keyword Spotting for Bangla Handwritten Documents

Segmentation-Free Keyword Spotting for Bangla Handwritten Do...

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International Workshop on Frontiers in Handwriting recognition

作者： Xi Zhang Umapada Pal Chew Lim Tan School of Computing National University of Singapore Singapore Computer Vision And Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, a segmentation-free keyword spotting method is proposed for Bangla handwritten documents. In order to tolerate large variations in handwritten scenarios, we extracted key points based on SIFT key point detector, and the end and intersection points found by morphological operations. Heat Kernel signature (HKS) is used to present the local characteristics of detected key points. Instead of using the same size of patch for all the key points, we apply a method dynamically deciding the patch size. Furthermore, our spotting method reduces the scope of searching on the document by only considering the candidate local zones with similar candidate key points, and does not need pre-processing steps. From the experiment on Bangla handwritten text we obtained encouraging results.

关键词： Feature extraction Entropy Hidden Markov models Detectors Heating Kernel Indexes

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An efficient algorithm for detection of road-like structures in satellite images

An efficient algorithm for detection of road-like structures...

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International Conference on pattern recognition

作者： A. Mukherjee S.K. Parui D. Chaudhuri B.B. Chaudhuri R. Krishnan Indian Statistical Institute Computer Vision and Pattern Recognition Unit Calcutta India Advanced Data Processing Research Institute Secunderabad India

Road networks are important features of satellite imagery. The main contribution of the present road detection method consists of an effective enhancement technique and an efficient segmentation technique that removes non-road pixels step by step from the image where parameters involved: in each step images are determined by the sensor characteristics (like spatial resolution and spectral range) of the satellite. Also, the segmentation process depends not only on the road contrast but also on the road length. Thus, a low contrast but long road segment does not get removed. We have tested the algorithm on a number of images from IRS and SPOT satellites and the results are satisfactory.

关键词： Satellites Roads Image segmentation Pixel Sensor phenomena and characterization Spatial resolution Joining processes Intelligent networks computer vision pattern recognition

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Shape Code Based Word-Image Matching for Retrieval of indian Multi-lingual Documents

Shape Code Based Word-Image Matching for Retrieval of Indian...

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International Conference on pattern recognition

作者： Arundhati Tarafdar Ranju Mondal Srikanta Pal Umapada Pal Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9781424475421

In the current scenario retrieving information from document images is a challenging problem. In this paper we propose a shape code based word-image matching (word-spotting) technique for retrieval of multilingual documents written in indian languages. Here, each query word image to be searched is represented by a primitive shape code using (i) zonal information of extreme points (ii) vertical shape based feature (iii) crossing count (with respect to vertical bar position) (iv) loop shape and position (v) background information etc. Each candidate word (a word having similar aspect ratio and topological feature to the query word) of the document is also coded accordingly. Then, an inexact string matching technique is used to measure the similarity between the primitive codes generated from the query word image and each candidate word of the document with which the query image is to be searched. Based on the similarity score, we retrieve the document where the query image is found. Experimental results on Bangla, Devnagari and Gurumukhi scripts document image databases confirm the feasibility and efficiency of our proposed approach.

关键词： Shape Encoding Image coding Image segmentation Feature extraction pattern recognition computer vision

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A New Symmetry Based on Proximity of Wavelet-Moments for Text Frame Classification in Video

A New Symmetry Based on Proximity of Wavelet-Moments for Tex...

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International Conference on pattern recognition

作者： Palaiahnakote Shivakumara Anjan Dutta Chew Lim Tan Umapada Pal School of Computing National University of Singapore Singapore Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper proposes the use of a new symmetry property based on proximity of the median moments in the wavelet domain. The method divides a given frame into 16 equally sized blocks to classify the true text frame. The average of high frequency subbands of a block is used for computing median moments to brighten the text pixel in a block of video frame. Then K-means clustering with K=2 is applied on the median moments of the block to classify it as a probable text block. For classified blocks, average wavelet median moments are computed for a sliding window. We introduce Max-Min cluster to classify the probable text pixel in each probable text block. The four quadrants are formed from the centroid of the probable text pixels. The new concept called symmetry is introduced to identify the true text block based on proximity between probable text pixels in each quadrant. If the frame produces at least one true text block, it is considered as a text frame otherwise a non-text frame. The method is tested on three datasets to evaluate the robustness of the method in classification of text frames in terms of recall and precision.

关键词： Pixel Chromium Feature extraction pattern recognition Robustness Silicon Classification algorithms

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A New Method for Detecting Altered Text in Document Images 2nd

A New Method for Detecting Altered Text in Document Images

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2nd International Conference on pattern recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Lopresti, Daniel Seraogi, Bhagesh Chaudhuri, Bidyut B. 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 Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9783030598297

As more and more office documents are captured, stored, and shared in digital format, and as image editing software becomes increasingly more powerful, there is a growing concern about document authenticity. For example, texts in property documents can be altered to make an illegal deal, or the date on an airline ticket can be altered to gain entry to airport terminals by breaching security. To prevent such illicit activities, this paper presents a new method for detecting altered text in a document. The proposed method explores the relationship between positive and negative coefficients of a DCT to extract the effect of distortions caused by tampering operations. Here we divide DCT coefficients into positive and negative classes, then reconstructs images from the inverse DCT of the respective positive and negative coefficients. Next, we perform Laplacian filtering over reconstructed images for widening the gap between the values of text and other pixels. Then filtered images of positive and negative coefficients are fused by an average operation. For a fused image, we generate Canny and Sobel edge images in order to investigate the effect of distortion through quality measures, namely, MSE, PSNR and SSIM used as features. In addition, for the fused image, the proposed method extracts features based on histograms over the residual images. The features are then passed on to a deep Convolutional Neural Network for classification. The proposed method is tested on our own dataset as well as two standard datasets, namely IMEI and the ICPR 2018 Fraud Contest dataset. The results show that the proposed method is effective and outperforms existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Inverse problems

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A complete automatic short answer assessment system with student identification

A complete automatic short answer assessment system with stu...

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International Conference on Document Analysis and recognition

作者： Hemmaphan Suwanwiwat Michael Blumenstein Umapada Pal School of Information and Communication Technology Griffith University Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

There are only a few studies undertaken in developing automatic assessment systems using handwriting recognition, even though a successful system would undoubtedly benefit the education system as schools and universities in many countries still employ paper-based examinations. To the best of the authors' knowledge, there is no existing work on an automatic off-line short answer assessment system comprising a student identification component. Hence in this paper, the authors propose a system towards this, where a new feature extraction technique called the Enhanced Water Reservoir, Loop and Gaussian Grid Feature, as well as other enhanced feature extraction techniques were utilised. Artificial Neural Networks and Support Vector Machines were employed as the classifiers; they were used for the investigation, and a comparison of the recognition and accuracy rates of the proposed systems, as well as the feature extraction techniques, was undertaken. The proposed assessment system achieved a recognition rate of 87.12% with 91.12% assessment accuracy, and the student identification component obtained a recognition rate of 99.52% with a 100% identification accuracy rate.

关键词： Feature extraction Field effect transistors Erbium

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Bengali Noun Morphological Analyzer

Bengali Noun Morphological Analyzer

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International Conference on Advances in Computing, Communications and Informatics

作者： Priyanka Das Arjun Das Dept. of Computational Linguistics The English and Foreign Languages University Computer Vision and Pattern Recognition Unit Indian statistical Institute

ISBN: (纸本)9781467361262

This paper describes a linguistics approach towards development of a Bengali Noun Morphological Analyzer implemented at first on the semi-manually created database of 87697 inflected words list tokens, i.e. Input2 for Linguistics Resource Creation comprising of Noun, Pronoun, Adjective roots with and without its suffixes. Then after the first implementation the developed Linguistic Resource knowledge is applied on an unknown Bengali corpus database containing 6157 tokens. At the initial stage of this research a linguistic analysis is done which leads to framing of the nominal suffix list which is later on used in nominal suffix extraction. This linguistic knowledge is implemented in developing the finite-state transducer grammar for Linguistic Resource which gives way to the development of Bengali Noun Morphological Analyzer. The final output obtained is around 44% accuracy. This accuracy can be always improved with time if we keep on increasing the nominal roots in the FST grammar file.

关键词： Morphological Analyzer Finite-State Transducer Grammar Nominal Suffix Xerox Finite-State Tool Compiler Lookup Tool

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Short Answer Question Examination using an Automatic Off-line Handwriting recognition System and a Novel Combined Feature

Short Answer Question Examination using an Automatic Off-lin...

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International Joint Conference on Neural Networks

ISBN: (纸本)9781479919611

Off-line automatic assessment systems can be an aid for teachers in the marking process. There has been no recent work in the development of off-line automatic assessment systems using handwriting recognition, even though such systems will clearly benefit the education sector. The reason is many schools and universities in many parts of the world still use paper-based examination. This research proposes the use of a newly developed feature extraction technique called the Modified Water Reservoir, Loop and Gaussian Grid Feature, as well as other feature extraction techniques. These techniques were investigated employing artificial neural networks and support vector machines as classifiers to develop an automatic assessment system for marking short answer questions. The system has high assessment accuracy (up to 94.75% for hand printed, 96.09% for cursive handwritten, and 95.71% for hand printed and cursive handwritten combined). The proposed system also includes assessment criteria to augment its accuracy.

关键词： Handwriting recognition Off-line Automatic Assessment System Modified direction feature Gaussian grid feature Water Reservoir Feature Modified Water Reservoir Loop and Gaussian Grid Feature handwriting recognition AUTOMATIC Feature extraction OFF LINE Hand Answer Classifiers Support Vector Network

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