DOCSLIB.ORG

Convolutional Neural Network Based Handwritten Bengali and Bengali-English Mixed Numeral Recognition

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Convolutional Neural Network Based Handwritten Bengali and Bengali-English Mixed Numeral Recognition I.J. Image, Graphics and Signal Processing, 2016, 9, 40-50 Published Online September 2016 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijigsp.2016.09.06 Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition M. A. H. Akhand, Mahtab Ahmed Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET) Khulna-9203, Bangladesh Email: {akhand, mahtab}@cse.kuet.ac.bd M. M. Hafizur Rahman Dept. of Computer Science, KICT, International Islamic University Malaysia (IIUM) Selangor, Malaysia Email: [email protected] Abstract—Recognition of handwritten numerals has progress in Roman, Chinese, and Arabic script [3, 4]. On gained much interest in recent years due to its various the other hand, recognition of handwritten Bengali potential applications. Bengali is the fifth ranked among numeral is largely neglected although it is a major the spoken languages of the world. However, due to language in Indian subcontinent having fifth ranked in the inherent difficulties of Bengali numeral recognition, a world and is the main language of Bangladesh. Some very few study on handwritten Bengali numeral Bengali numerals have very similar shape, and even in recognition is found with respect to other major the printed form few differs very little than other languages. The existing Bengali numeral recognition numerals. Therefore, Bengali numeral recognition is a methods used distinct feature extraction techniques and challenging task. various classification tools. Recently, convolutional An interesting aspect of Bengali documents is that neural network (CNN) is found efficient for image English entries are commonly available in those. These classification with its distinct features. In this paper, we include the text books written in Bengali scripts often have investigated a CNN based Bengali handwritten have entries in English especially the numerals; numeral recognition scheme. Since English numerals are Bangladeshi currencies contain both Bengali and English frequently used with Bengali numerals, handwritten numerals to represent values; handwritten Bengali- Bengali-English mixed numerals are also investigated in English mixed numerals are frequently found in this study. The proposed scheme uses moderate pre- Bangladesh while writing postal code, bank cheque, age, processing technique to generate patterns from images of number plate, mobile number and other tabular form handwritten numerals and then employs CNN to classify documents. Moreover, often people casually enter one or individual numerals. It does not employ any feature more English numerals which results a mixed-script extraction method like other related works. The proposed situation. There is a similarity in writing style of several method showed satisfactory recognition accuracy on the Bengali numerals (e.g., ‗০ ‘, ‗২ ‘,‘৪ ‘ and ‗৭ ‘) with benchmark data set and outperformed other prominent English numerals (e.g., ‗0‘ ,‘2‘, ‗8‘ and ‗9‘) that makes existing methods for both Bengali and Bengali-English Bengali-English mixed numeral recognition more mixed cases. challenging. The reason behind the uses of this mixed case is that English is used in parallel with Bengali in Index Terms—Image Pre-processing, Convolutional official works and education systems. Therefore, Bengali- Neural Network, Bengali Numeral, Handwritten Numeral English mixed numeral recognition system is challenging Recognition. and important for practical applications. Although several remarkable works are available for Bengali and English handwritten numeral recognition separately, a very few I. INTRODUCTION works are available for mixed Bengali-English numeral recognition whose performance are not at satisfactory Recognition of handwritten numerals has gained much level. interest in recent years due to its various potential The rest of the paper is organized as follows. Section II applications. These include postal system automation, reviews several related works and explains motivation of passports & travel document analysis, automatic bank the present study. Section III explains proposed cheque processing, and even for number plate recognition scheme using convolutional neural network identification [1, 2]. Research on recognition of (CNN) which contains dataset preparation, pre- unconstrained handwritten numerals has made impressive processing and classification. Section IV presents Copyright © 2016 MECS I.J. Image, Graphics and Signal Processing, 2016, 9, 40-50 Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition 41 experimental results of the proposed method and Recently, Wen and He [13] proposed a kernel and comparison of performance with other related works. Bayesian Discriminant based method to recognize Finally, a brief conclusion of the work is given in Section handwritten Bengali numeral. Most recently, Nasir and V. Uddin [14] proposed a hybrid system for recognition of handwritten Bengali numeral for the automated postal system, which performed feature extraction using k- II. RELATED WORKS means clustering, Baye‘s theorem and maximum of a Posteriori, then the recognition is performed using SVM. A few notable works are available for Bengali On the other hand, according to best of our knowledge, handwritten numeral recognition with respect to other a very few studies are available for Bengali-English popular Indian subcontinent scripts such as Devanagari mixed handwritten numeral recognition. Mustafi et al. [15] [5-7]. Bashar et al. [8] investigated a digit recognition proposed a system which extract topological and system based on windowing and histogram techniques. structural features of the handwritten Bengali and English Windowing technique is used to extract uniform features numerals using water overflow from the reservoir. For from scanned image files and then histogram is produced recognition, a MLP is trained with the extracted feature from the generated features. Finally, recognition of the values using Back-propagation (BP) algorithm. They digit is performed on the basis of generated histogram. considered 16 classes, considering similarity between Pal et al. [3] introduced a new technique based on the Bengali numerals ‗ ‘, ‗ ‘, ‗ ‘, ‗ ‘ with English concept of water overflow from the reservoir for feature ০ ২ ৪ ৭ numerals ‗0‘, ‗2‘, ‗8‘, ‗9‘, respectively, in same class. extraction and then employed binary tree classifier for Vazda et al. [16] investigated mixed Bengali-English handwritten Bengali numeral recognition. Basu et al. [4] numeral recognition in Indian postal document used Dempster-Shafer (DS) technique to combine the automation. At first they extracted numeral images from classification decisions obtained from two MLP based the destination address block section of a postal classifiers for handwritten Bengali numeral recognition document. They normalized the images into 28×28 pixel using two different feature sets. Feature sets they and train a 16 class MLP using these pixel values. To investigated are called shadow feature and centroid improve recognition accuracy, other two MLPs with 10 feature. Khan et al. [9] employed an evolutionary classes are considered for Bengali and English numeral approach to train artificial neural network for Bengali recognition. In recognition stage, at first the six digit pin- handwritten numeral. At first, they used boundary code is checked with 16 class MLP. If majority of the extraction on a numeral image in a single window by numerals are recognized as Bengali then the entire pin- horizontal-vertical scanning and scaled the image into code is considered to be written in Bengali and fixed sized matrix. Then, Multi-Layer Perceptron (MLP) recognized through 10 classes Bengali classifier. are evolved for recognition. Similarly, 10 class English classifier is used if the Wen et al. [10] proposed a handwritten Bengali majority of the numerals are recognized as English by the numeral recognition system for automatic letter sorting 16-class classifier. Bhattacharya and Chaudhuri [12] also machine. They used Support Vector Machine (SVM) investigated handwritten Bengali-English mixed numeral classifier combined with extensive feature extractor using recognition along with Bengali. They tested Bengali- Principal Component Analysis (PCA) and kernel PCA English mixed numerals in two different MLPs: a 16 (KPCA). Das et al. [11] also used SVM for classification class MLP as like previous studies and 20 class MLP but used different techniques for feature selection. Seven considering 10 classes for each of Bengali and English. different sets of variable sized local regions are generated The objective of this study is to develop a recognition using different region selection strategies in their work. A scheme for handwritten Bengali and Bengali-English genetic algorithm (GA) based region sampling strategy mixed numerals which is capable of providing high has been employed to select an optimal subset of local recognition accuracy. Toward this goal, we pre-processed regions containing high discriminating information about the handwritten images in a moderate way to generate the pattern shapes from the above mentioned set. patterns and then CNN is employed for classification of Bhattacharya and Chaudhuri [12] presented a Bengali and Bengali-English mixed handwritten numerals. multistage cascaded recognition scheme using wavelet- Recently, CNN is found efficient for image classification based multi-resolution representations and MLP with
Load more

Recommended publications
  • Cross-Cultural Research
    Cross-Cultural Research http://ccr.sagepub.com Cultural Adaptations After Progressionism Lauren W. McCall Cross-Cultural Research 2009; 43; 62 DOI: 10.1177/1069397108328613 The online version of this article can be found at: http://ccr.sagepub.com/cgi/content/abstract/43/1/62 Published by: http://www.sagepublications.com On behalf of: Society for Cross-Cultural Research Additional services and information for Cross-Cultural Research can be found at: Email Alerts: http://ccr.sagepub.com/cgi/alerts Subscriptions: http://ccr.sagepub.com/subscriptions Reprints: http://www.sagepub.com/journalsReprints.nav Permissions: http://www.sagepub.com/journalsPermissions.nav Citations http://ccr.sagepub.com/cgi/content/refs/43/1/62 Downloaded from http://ccr.sagepub.com at DUKE UNIV on January 9, 2009 Cross-Cultural Research Volume 43 Number 1 February 2009 62-85 © 2009 Sage Publications Cultural Adaptations After 10.1177/1069397108328613 http://ccr.sagepub.com hosted at Progressionism http://online.sagepub.com Lauren W. McCall National Evolutionary Synthesis Center How should behavioral scientists interpret apparently progressive stages of cultural history? Adaptive progress in biology is thought to only occur locally, relative to local conditions. Just as evolutionary theory offers physi- cal anthropologists an appreciation of global human diversity through local adaptation, so the metaphor of adaptation offers behavioral scientists an appreciation of cultural diversity through analogous mechanisms. Analyses reported here test for cultural adaptation in both biotic and abiotic environ- ments. Testing cultural adaptation to the human-made environment, the culture’s pre-existing technical complexity is shown to be a predictive fac- tor. Then testing cultural adaptation to the physical environment, this article corroborates Divale’s (1999) finding that counting systems are adaptations to unstable environments, and expands the model to include other environ- mental indices and cultural traits.
    [Show full text]
  • Handwriting Recognition in Indian Regional Scripts: a Survey of Offline Techniques
    1 Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques UMAPADA PAL, Indian Statistical Institute RAMACHANDRAN JAYADEVAN, Pune Institute of Computer Technology NABIN SHARMA, Indian Statistical Institute Offline handwriting recognition in Indian regional scripts is an interesting area of research as almost 460 million people in India use regional scripts. The nine major Indian regional scripts are Bangla (for Bengali and Assamese languages), Gujarati, Kannada, Malayalam, Oriya, Gurumukhi (for Punjabi lan- guage), Tamil, Telugu, and Nastaliq (for Urdu language). A state-of-the-art survey about the techniques available in the area of offline handwriting recognition (OHR) in Indian regional scripts will be of a great aid to the researchers in the subcontinent and hence a sincere attempt is made in this article to discuss the advancements reported in this regard during the last few decades. The survey is organized into different sections. A brief introduction is given initially about automatic recognition of handwriting and official re- gional scripts in India. The nine regional scripts are then categorized into four subgroups based on their similarity and evolution information. The first group contains Bangla, Oriya, Gujarati and Gurumukhi scripts. The second group contains Kannada and Telugu scripts and the third group contains Tamil and Malayalam scripts. The fourth group contains only Nastaliq script (Perso-Arabic script for Urdu), which is not an Indo-Aryan script. Various feature extraction and classification techniques associated with the offline handwriting recognition of the regional scripts are discussed in this survey. As it is important to identify the script before the recognition step, a section is dedicated to handwritten script identification techniques.
    [Show full text]
  • Mathematics in African History and Cultures
    Paulus Gerdes & Ahmed Djebbar MATHEMATICS IN AFRICAN HISTORY AND CULTURES: AN ANNOTATED BIBLIOGRAPHY African Mathematical Union Commission on the History of Mathematics in Africa (AMUCHMA) Mathematics in African History and Cultures Second edition, 2007 First edition: African Mathematical Union, Cape Town, South Africa, 2004 ISBN: 978-1-4303-1537-7 Published by Lulu. Copyright © 2007 by Paulus Gerdes & Ahmed Djebbar Authors Paulus Gerdes Research Centre for Mathematics, Culture and Education, C.P. 915, Maputo, Mozambique E-mail: [email protected] Ahmed Djebbar Département de mathématiques, Bt. M 2, Université de Lille 1, 59655 Villeneuve D’Asq Cedex, France E-mail: [email protected], [email protected] Cover design inspired by a pattern on a mat woven in the 19th century by a Yombe woman from the Lower Congo area (Cf. GER-04b, p. 96). 2 Table of contents page Preface by the President of the African 7 Mathematical Union (Prof. Jan Persens) Introduction 9 Introduction to the new edition 14 Bibliography A 15 B 43 C 65 D 77 E 105 F 115 G 121 H 162 I 173 J 179 K 182 L 194 M 207 N 223 O 228 P 234 R 241 S 252 T 274 U 281 V 283 3 Mathematics in African History and Cultures page W 290 Y 296 Z 298 Appendices 1 On mathematicians of African descent / 307 Diaspora 2 Publications by Africans on the History of 313 Mathematics outside Africa (including reviews of these publications) 3 On Time-reckoning and Astronomy in 317 African History and Cultures 4 String figures in Africa 338 5 Examples of other Mathematical Books and 343
    [Show full text]
  • Arxiv:2010.11856V3 [Cs.CL] 13 Apr 2021 Questions from Non-English Native Speakers to Rep- Information-Seeking Questions—Questions from Resent Real-World Applications
    XOR QA: Cross-lingual Open-Retrieval Question Answering Akari Asaiº, Jungo Kasaiº, Jonathan H. Clark¶, Kenton Lee¶, Eunsol Choi¸, Hannaneh Hajishirziº¹ ºUniversity of Washington ¶Google Research ¸The University of Texas at Austin ¹Allen Institute for AI {akari, jkasai, hannaneh}@cs.washington.edu {jhclark, kentonl}@google.com, [email protected] Abstract ロン・ポールの学部時代の専攻は？[Japanese] (What did Ron Paul major in during undergraduate?) Multilingual question answering tasks typi- cally assume that answers exist in the same Multilingual document collections language as the question. Yet in prac- (Wikipedias) tice, many languages face both information ロン・ポール (ja.wikipedia) scarcity—where languages have few reference 高校卒業後はゲティスバーグ大学へ進学。 (After high school, he went to Gettysburg College.) articles—and information asymmetry—where questions reference concepts from other cul- Ron Paul (en.wikipedia) tures. This work extends open-retrieval ques- Paul went to Gettysburg College, where he was a member of the Lambda Chi Alpha fraternity. He tion answering to a cross-lingual setting en- graduated with a B.S. degree in Biology in 1957. abling questions from one language to be an- swered via answer content from another lan- 生物学 (Biology) guage. We construct a large-scale dataset built on 40K information-seeking questions Figure 1: Overview of XOR QA. Given a question in across 7 diverse non-English languages that Li, the model finds an answer in either English or Li TYDI QA could not find same-language an- Wikipedia and returns an answer in English or L . L swers for. Based on this dataset, we introduce i i is one of the 7 typologically diverse languages.
    [Show full text]
  • Bengali Handwritten Numeral Recognition Using Artificial Neural Network and Transition Elements
    BENGALI HANDWRITTEN NUMERAL RECOGNITION USING ARTIFICIAL NEURAL NETWORK AND TRANSITION ELEMENTS a,* b c Zahidur Rahim Chowdhury Mohammad Abu Naser Ashraf Bin Islam a United International University, Dhaka. b Islamic University of Technology, Gajipur. c Bangladesh University of Engineering and Technology, Dhaka. * Corresponding email address: [email protected] Abstract: Bengali hand-writing recognition has potential application in document processing for one the widely used for language in the world. A method using Artificial Neural Network (ANN) is utilized primarily to identify numerals of the language using transition features. Maximum accuracy of 82% is reported in this article for an optimized network. The typical performance of the handwriting recognition system that uses a single recognition scheme is around 85%. The significance of local features in a character should be incorporated to enhance the overall performance of the network. Key words: Bengali Hand-writing, Numeral, Pattern Recognition, Neural Network, Transition. article. Recognition results from different systems were INTRODUCTION compared to make the final decision. The average recognition rate, error rate and reliability achieved by the Hand-written Bengali character recognition is a integrated system were 95.05%, 0.3% and 99.03%, process where techniques of pattern recognition are applied respectively. to analyze handwritings of Bengali language, one of the In this article, a recognition process for Bengali hand most popular languages in the world. Beside Bengali, written numerals is presented using ‘holistic’ approaches researchers have studied the recognition process using due to limited number of possible outputs. Transition different techniques for other popular languages like features of an image, instead of the complete image, were English [1–4], Chinese [5], Arabic [6], Japanese [7, 8], and used as inputs of the ANN for an efficient and a compact Indic [9].
    [Show full text]
  • LNCS 8104, Pp
    Bengali Printed Character Recognition – A New Approach Soharab Hossain Shaikh1, Marek Tabedzki2, Nabendu Chaki3, and Khalid Saeed4 1 A.K.Choudhury School of Information Technology, University of Calcutta, India [email protected] 2 Faculty of Computer Science, Bialystok University of Technology, Poland [email protected] 3 Department of Computer Science & Engineering, University of Calcutta, India [email protected] 4 Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Cracow, Poland [email protected] Abstract. This paper presents a new method for Bengali character recognition based on view-based approach. Both the top-bottom and the lateral view-based approaches have been considered. A layer-based methodology in modification of the basic view-based processing has been proposed. This facilitates handling of unequal logical partitions. The document image is acquired and segmented to extract out the text lines, words, and letters. The whole image of the individual characters is taken as the input to the system. The character image is put into a bounding box and resized whenever necessary. The view-based approach is applied on the resultant image and the characteristic points are extracted from the views after some preprocessing. These points are then used to form a feature vector that represents the given character as a descriptor. The feature vectors have been classified with the aid of k-NN classifier using Dynamic Time Warping (DTW) as a distance measure. A small dataset of some of the compound characters has also been considered for recognition. The promising results obtained so far encourage the authors for further work on handwritten Bengali scripts.
    [Show full text]
  • Online Bengali Handwritten Numerals Recognition Using Deep Autoencoders
    Online Bengali Handwritten Numerals Recognition Using Deep Autoencoders Arghya Pal∗, B. K. Khonglahy, S. Mandaly, Himakshi Choudhuryy, S. R. M. Prasanna y, H. L. Rufinerz and Vineeth N Balasubramanian∗ Department of Computer Science and Engineering Indian Institute of Technology Hyderabad, Telangana 502285 ∗ Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati, Guwahati-781039 y Facultad de Ingeniera y Ciencias Hdricas - Universidad Nacional del Litoral - CONICET, Ciudad Universitaria, Paraje El Pozo, S3000 Santa Fe, Argentina z E-mail: fcs15resch11001, [email protected], fbanriskhem, [email protected], lrufi[email protected] Abstract—This work describes the development of online handwritten Bengali numeral recognition. This work finds its handwritten isolated Bengali numerals using Deep Autoencoder place in various domain like online form filling applications, (DA) based on Multilayer perceptron (MLP) [1]. Autoencoders census data collection, banking operations, number dialing capture the class specific information and the deep version uses many hidden layers and a final classification layer to accomplish systems and many more. this. DA based on MLP uses the MLP training approach for Some of the works that recognizes online handwritten its training. Different configurations of the DA are examined characters from different perspectives include HMM based to find the best DA classifier. Then an optimization technique handwriting recognition systems for Bangla [3], Tamil [4], have been adopted to reduce the overall weight space of the Telugu [5], Assamese [6], SVM based system for Tamil [7] DA based on MLP that in turn makes it suitable for a real time application. The performance of the DA based system is and Devanagari [8].
    [Show full text]
  • The Evolution of the Printed Bengali Character
    The Evolution of the Printed Bengali Character from 1778 to 1978 by Fiona Georgina Elisabeth Ross School of Oriental and African Studies University of London Thesis presented for the degree of Doctor of Philosophy 1988 ProQuest Number: 10731406 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. ProQuest 10731406 Published by ProQuest LLC (2017). Copyright of the Dissertation is held by the Author. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code Microform Edition © ProQuest LLC. ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 20618054 2 The Evolution of the Printed Bengali Character from 1778 to 1978 Abstract The thesis traces the evolution of the printed image of the Bengali script from its inception in movable metal type to its current status in digital photocomposition. It is concerned with identifying the factors that influenced the shaping of the Bengali character by examining the most significant Bengali type designs in their historical context, and by analyzing the composing techniques employed during the past two centuries for printing the script. Introduction: The thesis is divided into three parts according to the different methods of type manufacture and composition: 1. The Development of Movable Metal Types for the Bengali Script Particular emphasis is placed on the early founts which lay the foundations of Bengali typography.
    [Show full text]
  • Sino-Tibetan Numeral Systems: Prefixes, Protoforms and Problems
    Sino-Tibetan numeral systems: prefixes, protoforms and problems Matisoff, J.A. Sino-Tibetan Numeral Systems: Prefixes, Protoforms and Problems. B-114, xii + 147 pages. Pacific Linguistics, The Australian National University, 1997. DOI:10.15144/PL-B114.cover ©1997 Pacific Linguistics and/or the author(s). Online edition licensed 2015 CC BY-SA 4.0, with permission of PL. A sealang.net/CRCL initiative. PACIFIC LINGUISTICS FOUNDING EDITOR: Stephen A. Wunn EDITORIAL BOARD: Malcolm D. Ross and Darrell T. Tryon (Managing Editors), Thomas E. Dutton, Nikolaus P. Himmelmann, Andrew K. Pawley Pacific Linguistics is a publisher specialising in linguistic descriptions, dictionaries, atlases and other material on languages of the Pacific, the Philippines, Indonesia and southeast Asia. The authors and editors of Pacific Linguistics publications are drawn from a wide range of institutions around the world. Pacific Linguistics is associated with the Research School of Pacific and Asian Studies at the Australian National University. Pacific Linguistics was established in 1963 through an initial grant from the Hunter Douglas Fund. It is a non-profit-making body financed largely from the sales of its books to libraries and individuals throughout the world, with some assistance from the School. The Editorial Board of Pacific Linguistics is made up of the academic staff of the School's Department of Linguistics. The Board also appoints a body of editorial advisors drawn from the international community of linguists. Publications in Series A, B and C and textbooks in Series D are refereed by scholars with re levant expertise who are normally not members of the editorial board.
    [Show full text]
  • Introducing the Sylheti Language and Its Speakers, and the SOAS Sylheti Project
    Language Documentation and Description ISSN 1740-6234 ___________________________________________ This article appears in: Language Documentation and Description, vol 18. Editors: Candide Simard, Sarah M. Dopierala & E. Marie Thaut Introducing the Sylheti language and its speakers, and the SOAS Sylheti project CANDIDE SIMARD, SARAH M. DOPIERALA & E. MARIE THAUT Cite this article: Simard, Candide, Sarah M. Dopierala & E. Marie Thaut. 2020. Introducing the Sylheti language and its speakers, and the SOAS Sylheti project. In Candide Simard, Sarah M. Dopierala & E. Marie Thaut (eds.) Language Documentation and Description 18, 1-22. London: EL Publishing. Link to this article: http://www.elpublishing.org/PID/196 This electronic version first published: August 2020 __________________________________________________ This article is published under a Creative Commons License CC-BY-NC (Attribution-NonCommercial). The licence permits users to use, reproduce, disseminate or display the article provided that the author is attributed as the original creator and that the reuse is restricted to non-commercial purposes i.e. research or educational use. See http://creativecommons.org/licenses/by-nc/4.0/ ______________________________________________________ EL Publishing For more EL Publishing articles and services: Website: http://www.elpublishing.org Submissions: http://www.elpublishing.org/submissions Introducing the Sylheti language and its speakers, and the SOAS Sylheti project Candide Simard The University of the South Pacific Sarah M. Dopierala Goethe University Frankfurt E. Marie Thaut SOAS, University of London Abstract Sylheti is a minoritised, politically unrecognised, and understudied Eastern Indo-Aryan language with approximately 11 million speakers worldwide, with high speaker concentrations in the Surma and Barak river basins in north-eastern Bangladesh and south Assam, India, and in several diasporic communities around the world (especially UK, USA, and Middle East).
    [Show full text]
  • Bengali Digit Recognition from Real-World Images Debdyut Hajra, B
    Bengali Digit Recognition from Real-World Images Debdyut Hajra, B. P. Poddar Institute of Management and Technology, India Introduction Proposed Solution Training Objectives I have used LeNet5 model with inception and dropout for deep Bengali is the 4th most popular language in the world and 2nd in learning. I have applied the model on the above-mentioned datasets. This project explores the development of a complete Bengali digit India. It is mostly spoken in Bangladesh and in the states of West I have obtained the highest accuracy of 94.7% for Bengali numerals recognition for scene images. Following are our objectives: Bengal, Assam and Tripura in India, as indicated by the figure on the corresponding to the CMATERdb numeral dataset. The total training 1. Develop a robust image pre-processing pipeline to extract the time was approximately 20 minutes for a training set of 4000 images, right. It is spoken by over 200 million people across the world. The desired features of the characters from the image. Bengali language has a very rich character set. There are 10 validation set and test set of 1000 images each. Intel optimised 2. Focus primarily on achieving a sufficiently high accuracy for digit numerals and 50 Bengali basic characters. There are also over 100 TensorFlow library has been used for building the classifier. recognition which can be used for practical purposes. compound characters in Bengali. Hence, it is challenging to create an 3. Implement the concepts developed in a web-application and Application Development efficient optical character recognizer (OCR) for the same. The deploy the trained model for practical use.
    [Show full text]
  • Offline Handwritten Gurmukhi Script Recognition
    Offline Handwritten Gurmukhi Script Recognition A Thesis Submitted in fulfillment of the requirements for the award of the degree of Doctor of Philosophy Submitted by Munish Kumar (Registration No. 950811010) Under the supervision of Dr. R. K. Sharma Dr. Manish Kumar Professor, Associate Professor, Thapar University, Panjab University Regional Centre, Patiala Muktsar Thapar University, Patiala School of Mathematics and Computer Applications Thapar University Patiala–147004 (Punjab) India July, 2014 i Abstract Over the last few years, a good number of laboratories all over the world have been involved in research on handwriting recognition. Handwriting recognition is a complex problem owing to the issues of variations in writing styles and size of the characters etc. The main objective of this work is to develop an offline handwritten Gurmukhi script recognition system. Gurmukhi is the script used for writing Punjabi language which is widely spoken in certain regions of north India. This thesis is divided into eight chapters. A brief outline of each chapter is given in the following paragraphs. The first chapter introduces the process of OCR and various phases of OCR like digitization, pre-processing, segmentation, feature extraction, classification and post- processing. Applications of offline handwritten character recognition system are also discussed in this chapter. In an overview of Gurmukhi script, the nature of handwriting in Gurmukhi script and character set of Gurmukhi script has also been presented. Major contributions and assumptions in this research work have also been discussed in this chapter. Chapter 2 contains a review of literature on various methods used for non-Indian and Indian scripts recognition.
    [Show full text]