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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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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...

引用

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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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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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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ICDAR2015 Competition on Video Script Identification (CVSI 2015)

ICDAR2015 Competition on Video Script Identification (CVSI 2...

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International Conference on Document Analysis and recognition

作者： Nabin Sharma Ranju Mandal Rabi Sharma Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479918065

This paper presents the final results of the ICDAR 2015 Competition on Video Script Identification. A description and performance of the participating systems in the competition are reported. The general objective of the competition is to evaluate and benchmark the available methods on word-wise video script identification. It also provides a platform for researchers around the globe to particularly address the video script identification problem and video text recognition in general. The competition was organised around four different tasks involving various combinations of scripts comprising tri-script and multiscript scenarios. The dataset used in the competition comprised ten different scripts. In total, six systems were received from five participants over the tasks offered. This report details the competition dataset specifications, evaluation criteria, summary of the participating systems and their performance across different tasks. The systems submitted by Google Inc. were the winner of the competition for all the tasks, whereas the systems received from Huazhong University of Science and Technology (HUST) and computer vision Center (CVC) were very close competitors.

关键词： Video scripts identification multi-lingual OCR multi-lingual video-text competition

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Performance evaluation of DTW and its variants for word spotting in degraded documents

Performance evaluation of DTW and its variants for word spot...

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International Conference on Document Analysis and recognition

作者： Tanmoy Mondal Nicolas Ragot Jean-Yves Ramel Umapada Pal Université François Rabelais Laboratory of Computer Science (LI) Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479918065

In word spotting literature, classical DTW has been widely employed. However there exists several other improved versions of DTW along with other robust sequence matching techniques. Very few of them have been studied in the context of word spotting and this scarcity of research work is the motivation of the paper. This paper presents a comparative study of classical Dynamic Time Warping (DTW) technique and many of its improved modifications, as well as other sequence matching techniques in the context of word spotting. An experimental study on historical documents is performed to evaluate the behavior of DTW's variants and other sequence matching techniques. A detailed comparative analysis along with wide range of experimentation is performed, which shows that classical DTW remains a good choice when there are no segmentation problems and when features are very local. In case of word segmentation errors, Continuous Dynamic Programming (CDP) seems to be a better choice. This research work has introduced several other improved sequence matching algorithms in the context of word spotting, which show interesting and improved results.

关键词： Biomedical imaging Biomedical measurement

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An approach for character segmentation of handwritten Bangla and devanagari script

An approach for character segmentation of handwritten Bangla...

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IEEE International Advance Computing Conference, IACC

作者： Anmol J Bhattad Bidyut B. Chaudhuri Electronics & Communication Engineering National Institute of Technology Karnataka Surathkal Mangalore India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479980482

In this paper a scheme for segmentation of unconstrained handwritten Devanagari and Bangla words into characters and its sub-parts is proposed. Firstly, the region above headline is identified by counting the number of white to black transitions in each row, which followed by its separation. Then the characters are segmented using fuzzy logic. For each column, the inputs to the fuzzy system are the location of first white pixel, thickness of the first black stroke, count of white pixels, and the run length count of white pixels.

关键词： Indexes Robustness

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Exemplary Sequence Cardinality: An Effective Application for Word Spotting

Exemplary Sequence Cardinality: An Effective Application for...

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International Conference on Document Analysis and recognition

作者： Tanmoy Mondal Nicolas Ragot Jean-Yves Ramel Umapada Pal Université Fran?ois Rabelais Tours Laboratory of Computer Science (LI) Tours France. Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India.

ISBN: (纸本)9781479918065

In this paper, a new sequence matching algorithm called as Exemplary Sequence Cardinality (ESC) is proposed. ESC combines several abilities of other sequence matching algorithms e.g. DTW, SSDTW, CDP, FSM, MVM, OSB~1. Depending on the application domain, ESC can be tuned to behave such as these different sequence matching algorithms. Its generality and robustness comes from its ability to find subsequences (as in CDP and SSDTW), to skip outliers inside the target sequences (as in MVM and FSM) and also in the query sequence (as in OSB) and it has the ability to have many to one and one to many correspondences (as in DTW) between the elements of the query and the target sequences. It's special characteristic of skipping noisy elements from query sequence along with other afore mentioned properties gives it an edge over FSM. In case of word spotting application, the outliers skipping capability of ESC makes it less sensible to local variations in the spelling of words, and also to noise present in the query and/or in the target word images. Due to it's capability of sub-sequence matching, the ESC algorithm has the ability to retrieve a query inside a line or piece of line. Finally, its multiple matching facilities (many to one and one to many matching) is proven to be well advantageous in case of different length of target and query sequences due to the variability in scale, font, type/size factors. By experimenting on printed historical document images, we have demonstrated the interest of proposed ESC algorithm in specific cases when incorrect word segmentation and word level local variations occur regularly.

关键词： Word spotting Elastic sequence matching Minimal variance matching (MVM) Optimal sequence bijection (OSB) Dynamic time warping (DTW) Continuous dynamic programming (CDP) Flexible sequence matching (FSM)

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New texture-spatial features for keyword spotting in video images

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

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Asian Conference on pattern recognition (ACPR)

作者： Palaiahnakote Shivakumara Guozhu Liang Sangheeta Roy Umapada Pal Tong Lu 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)9781479961016

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.

关键词： Image segmentation Semantics Video signal processing Indexing pattern recognition Spatial resolution

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