DOCSLIB.ORG

An Approach for Automatic Recognition System for Indian Vehicles Numbers Using K-Nearest Neighbours and Decision Tree Classifier^

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Approach for Automatic Recognition System for Indian Vehicles Numbers Using K-Nearest Neighbours and Decision Tree Classifier^ International Journal of Advanced Science and Technology Vol. 28, No. 9, (2019), pp. 477-492 An Approach for Automatic Recognition System for Indian Vehicles Numbers Using K-Nearest Neighbours and Decision Tree Classifier^ Ruchika Bindal1, Pradeepta Kumar Sarangi1*, Gagandeep Kaur1, Gamini Dhiman1 1Chitkara University Institute of Engineering and Technology Chitkara University, Punjab, India Abstract An Automatic Vehicle Number Plate Recognition System is presented in this paper. Basically, there are different kinds of motor vehicle number plates which are used in India. The actual number plates are not similar and most of them are deviated from the prescribed norms as laid by Government of India from time to time. The number plates of Indian vehicles are written in different languages also. The number plate characters are varied according to the language of a particular state in the country however a large portion of number plates are written in English language (legally all plates are required to be in modern Hindu-Arabic numerals with Latin letters). This is very vital in recognizing the number plates, extraction of number plate characters, segmenting the characters because of some diversity. In this paper we present a work on extraction of number plates, segmentation and recognition of number plates in the domain of English language. Prewitt filter technology is used for extracting the number plate thresholding and for segmentation of connected component analysis are used. For recognizing the characters, neural network, k-nearest neighbor and decision tree classifiers are used. The recognition rate of neural network is 96.1%, k-nearest neighbor is 95.55% and decision tree is 91.38% respectively which is acceptable for future research in this direction. Keywords— Indian Vehicle Number Plate, Pattern Recognition, k-Nearest Neighbor, Decision Tree, Neural Network, Local Histogram, Hierarchical Centroid. 1. INTRODUCTION Automatic Vehicle Number Plate Recognition (AVNPR) system [1] is an essential technique that is used in intelligent systems like transportation systems. Actually, this system uses the progressive technology of machine vision that uses the number plate of vehicles without any straight forward interference of humans. Because of its many applications, this is an important area for research work. The evolution of the intelligent systems basically provided from the data of number plates of the vehicles that can be used for further reasoning and supervising. Basically, there are many important areas where the AVNPR is used such as in traffic problems for collecting the tolls on highway, for security at borders and customs where there is a need of high security in the premises and so on, when the number plate is standard then the AVNPR system will not have any ISSN: 2005-4238 IJAST 477 Copyright ⓒ 2019 SERSC International Journal of Advanced Science and Technology Vol. 28, No. 9, (2019), pp. 477-492 difficulty for reading and recognizing the number plate. As we see that in India there are different models of number plates so it is difficult task in India. The work of AVNPR is generally wrapped into the following steps: Extract the region of interest of a number plate, segment characters and recognizing characters followed by the generation of a string which contains the actual characters in the number plate of a vehicle. The system captured the image of vehicle which is treated as input image from where the only area of number plate is observed and that can be used in further processing. In next step, segmentation of characters are done. In this, every character is segregated and disjointed. The character recognition is done on the basis of choice of the characters of outstanding features by using a recognition engine. 2. LITERATURE SURVEY In (Parasuraman, et al, 2010) [2], the method proposed is for the recognition of Indian number plates. Image is the only input for the system which is acquired by a camera. This method consists of following stages: RGB or colored to gray scale conversion, detection of vertical edges and binarization of image, analysis and dilation of image, vertical projection and thresholding, extraction of actual location of the number plate, apply filters and enhancing the image, binarization and smoothing process and segmentation of characters for horizontal and vertical division. In this method, the speedup is achieved in processing. The colored images are converted to the gray scale images based on RGB to gray scale conversion technique. Then the resulted images are converted to the binary images. Binarization is done to extract the number plate region from the acquired image [3]. The filtration is used to remove the unnecessary objects from the number plate. In analysis, it looks for the appropriate size of image, it calculates the values of height and the width. There are certain fixed height values, that is, maximum height and minimum height. If the object height is in the range then the object is retained and otherwise it removes other object. After that there are fixed width values for maximum and minimum width. If the object is in the width limit then the object is retained otherwise it removes other objects. The process of dilation is used to get the rough location of the number plates. After getting the accurate location, the extract and only required region of the number plate from which the starting and ending position of the number plates are identified. In (Du. et al., 2013) [4] in the recognition system of license plate shows the accuracy of ALPR system. They employed various techniques for license plate extraction. A. Using Boundary/Edge information As the number plates are usually in rectangular shape with a known aspect ratio, it can extract the number plate from all the rectangles that are in the image. It uses the Sobel filters as used to detect the edges. As there is the difference in color of car body and number plate, so the boundaries of number plate are shown by the edges. The outcome of this method yields 96.2% on images under the various conditions. B. Using global image information The Connected Component Analysis (CCA) technique is used in binary image processing. It scans the binary image and the pixels of the image are labeled into components that are based on pixel connectivity. C. Using Texture Features ISSN: 2005-4238 IJAST 478 Copyright ⓒ 2019 SERSC International Journal of Advanced Science and Technology Vol. 28, No. 9, (2019), pp. 477-492 This method depends on the text or the characters that are present on the number plate, which results the difference in gray scale level between the colors of the characters and the background color of the number plate. D. Using color feature Some countries have the specific color for their number plates. By this, the number can be extracted from the image by locating their respective color. According to (Rajput and Som, 2015) [5] the method followed have three phases: image capture, license plate localization and number recognition. In image capture phase, the captured image of the vehicle is normalized. The standard dimension to normalize an image is 400 x 300 pixels. Then the image is converted to the gray scale. In plate localization phase, the wavelet decomposition is performed to get the approximation. The number plate is located by the vertical and horizontal frequency energies. A Gaussian filter is used to modify the incoming signal with a Gaussian function. There are many benefits for using the Gaussian filter as it is rationally symmetric, there is decrease in variation of filter weights from the central peak; the central pixels have the highest weight, this filtering is separable, this means that the filters rotate the image with 1D horizontal filter and after that convolves it with 1D vertical filter. In the number plate recognition phase, to get the number plate the gray colored image, the image are converted into black and white image and sets the pixels intensity in the range of 0 and 255. After segmentation, the characters are normalized for refinement for no extra white pixels. In (Ram Lingam. et al., 2014) [6], the report is based on UK number plates and the misread plates. There are some defined parameters: A. Failed to capture and misreads The automatic number plate recognition system is failed to get the picture from the camera when the vehicle is passing. The "misread" is that when the OCR engine has failed to read the number plate correctly. B. Character spacing analysis from police data There are records of the images that are captured, some of them are legal and some are illegal. Illegal number plates are those which have illegal fonts, spacing. So, these numbers are analyzed from the police data. C. Character spacing Analysis from real world data In this, they have data from the real world that is they experimented with 2,200 number plates which were photographed by a digital camera. They have taken the data of two major airport car parking and send them to the police for safety to check them randomly and get the proportion of legal number plates and the number plate that have the illegal fonts. In the report, for the first time the vehicle number plate image is standardized (Chi et al., 2006) [7]. Scaling and again evaluation are tested for erasing the exception and ascertain clear specifications for SVM process. For character recognition, the SVM process has been used. SVM process have higher correct detection rate than the neural network systems. ISSN: 2005-4238 IJAST 479 Copyright ⓒ 2019 SERSC International Journal of Advanced Science and Technology Vol. 28, No. 9, (2019), pp. 477-492 In (Lopez. et al., 2007) [8] designed a system which is suitable in when the camera is in motion.
Load more

Recommended publications
  • Classification of Gujarati Documents Using Naïve Bayes Classifier
    ISSN (Print) : 0974-6846 Indian Journal of Science and Technology, Vol 10(5), DOI: 10.17485/ijst/2017/v10i5/103233, February 2017 ISSN (Online) : 0974-5645 Classification of Gujarati Documents using Naïve Bayes Classifier Rajnish M. Rakholia1* and Jatinderkumar R. Saini2 1School of Computer Science, R. K. University, Rajkot - 360020, Gujarat, India; [email protected] 2Narmada College of Computer Application, Bharuch - 392011, Gujarat, India; [email protected] Abstract Objectives: Information overload on the web is a major problem faced by institutions and businesses today. Sorting out some useful documents from the web which is written in Indian language is a challenging task due to its morphological variance Methods: Keyword search is a one of the way to retrieve the meaningful document from the web, but it doesn’t discriminate by context. In this paper and language barrier. As on date, there is no document classifier available for Gujarati language. we have presented the Naïve Bayes (NB) statistical machine learning algorithm for classification of Gujarati documents. Six pre-defined categories sports, health, entertainment, business, astrologyFindings: and spiritual The experimental are used for this results work. show A corpus that the of 280 Gujarat documents for each category is used for training and testing purpose of the categorizer. WeThese have results used k-foldprove cross validation to evaluate the performance of Naïve Bayes classifier. Applications: Proposed research work isaccuracy very useful of NB to classifier implement without the functionality and using features of directory selection search was in many75.74% web and portals 88.96% to sortrespectively. useful documents and many Informationthat the NB classifierRetrieval contribute(IR) applications.
    [Show full text]
  • Numerical Notation: a Comparative History
    This page intentionally left blank Numerical Notation Th is book is a cross-cultural reference volume of all attested numerical notation systems (graphic, nonphonetic systems for representing numbers), encompassing more than 100 such systems used over the past 5,500 years. Using a typology that defi es progressive, unilinear evolutionary models of change, Stephen Chrisomalis identifi es fi ve basic types of numerical notation systems, using a cultural phylo- genetic framework to show relationships between systems and to create a general theory of change in numerical systems. Numerical notation systems are prima- rily representational systems, not computational technologies. Cognitive factors that help explain how numerical systems change relate to general principles, such as conciseness and avoidance of ambiguity, which also apply to writing systems. Th e transformation and replacement of numerical notation systems relate to spe- cifi c social, economic, and technological changes, such as the development of the printing press and the expansion of the global world-system. Stephen Chrisomalis is an assistant professor of anthropology at Wayne State Uni- versity in Detroit, Michigan. He completed his Ph.D. at McGill University in Montreal, Quebec, where he studied under the late Bruce Trigger. Chrisomalis’s work has appeared in journals including Antiquity, Cambridge Archaeological Jour- nal, and Cross-Cultural Research. He is the editor of the Stop: Toutes Directions project and the author of the academic weblog Glossographia. Numerical Notation A Comparative History Stephen Chrisomalis Wayne State University CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Delhi, Dubai, Tokyo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521878180 © Stephen Chrisomalis 2010 This publication is in copyright.
    [Show full text]
  • ICCIASH-2020 PROCEEDINGS.Pdf
    St. MARTIN’S ENGINEERING COLLEGE An Autonomous Institute A Non Minority College | Approved by AICTE | Affiliated to JNTUH, Hyderabad | NAAC-Accredited„A+‟ Grade|2(f)&12(B)status(UGC)ISO9001:2008 Certified| NBA Accredited | SIRO (DSIR) | UGC-Paramarsh | Recognized Remote Center of IIT, Bombay Dhulapally, Secunderabad – 500100, Telangana State, India. www.smec.ac.in Department of Science & Humanities International Conference on “Continuity, Consistency and Innovation in Applied Sciences and Humanities” on 13th & 14th August 2020 (ICCIASH – 20) Editor in Chief Dr. P. Santosh Kumar Patra Principal, SMEC Editor Dr. Ranadheer Reddy Donthi Professor & Head, Dept. of S&H, SMEC Editorial Committee Dr. A. Aditya Prasad, Professor, S&H Dr. N. Panda, Professor, S&H Dr. S. Someshwar, Associate Professor,S&H Dr. Andrea S. C. D’Cruz, Associate Professor, S&H Dr. Venkanna, Associate Professor, S&H Mr. S. Malli Babu, Associate Professor, S&H Mr.K.Sathish,AssistantProfessor,S&H ISBN No. 978-93-88096-42-3 St. MARTIN’S ENGINEERING COLLEGE Dhulapally, Secunderabad - 500100 NIRF ranked, NAAC A+ ACCREDITED M. LAXMAN REDDY CHAIRMAN MESSAGE I am extremely pleased to know that the department of Science & Humanities of SMEC is organizing Online International Conference on “Continuity, Consistency and Innovation in Applied Sciences and Humanities (ICCIASH-2020)” on 13th and 14th August, 2020. I understand that large number of researchers have submitted their research papers for presentation in the conference and also for publication. The response to this conference from all over India and Foreign countries is most encouraging. I am sure all the participants will be benefitted by their interaction with their fellow researchers and engineers which will help for their research work and subsequently to the society at large.
    [Show full text]
  • Numbering Systems Developed by the Ancient Mesopotamians
    Emergent Culture 2011 August http://emergent-culture.com/2011/08/ Home About Contact RSS-Email Alerts Current Events Emergent Featured Global Crisis Know Your Culture Legend of 2012 Synchronicity August, 2011 Legend of 2012 Wednesday, August 31, 2011 11:43 - 4 Comments Cosmic Time Meets Earth Time: The Numbers of Supreme Wholeness and Reconciliation Revealed In the process of writing about the precessional cycle I fell down a rabbit hole of sorts and in the process of finding my way around I made what I think are 4 significant discoveries about cycles of time and the numbers that underlie and unify cosmic and earthly time . Discovery number 1: A painting by Salvador Dali. It turns that clocks are not as bad as we think them to be. The units of time that segment the day into hours, minutes and seconds are in fact reconciled by the units of time that compose the Meso American Calendrical system or MAC for short. It was a surprise to me because one of the world’s foremost authorities in calendrical science the late Dr.Jose Arguelles had vilified the numbers of Western timekeeping as a most grievious error . So much so that he attributed much of the worlds problems to the use of the 12 month calendar and the 24 hour, 60 minute, 60 second day, also known by its handy acronym 12-60 time. I never bought into his argument that the use of those time factors was at fault for our largely miserable human-planetary condition. But I was content to dismiss mechanized time as nothing more than a convenient tool to facilitate the activities of complex societies.
    [Show full text]
  • The Writing Revolution
    9781405154062_1_pre.qxd 8/8/08 4:42 PM Page iii The Writing Revolution Cuneiform to the Internet Amalia E. Gnanadesikan A John Wiley & Sons, Ltd., Publication 9781405154062_1_pre.qxd 8/8/08 4:42 PM Page iv This edition first published 2009 © 2009 Amalia E. Gnanadesikan Blackwell Publishing was acquired by John Wiley & Sons in February 2007. Blackwell’s publishing program has been merged with Wiley’s global Scientific, Technical, and Medical business to form Wiley-Blackwell. Registered Office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom Editorial Offices 350 Main Street, Malden, MA 02148-5020, USA 9600 Garsington Road, Oxford, OX4 2DQ, UK The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK For details of our global editorial offices, for customer services, and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley-blackwell. The right of Amalia E. Gnanadesikan to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks.
    [Show full text]
  • 3 Writing Systems
    Writing Systems 43 3 Writing Systems PETER T. DANIELS Chapters on writing systems are very rare in surveys of linguistics – Trager (1974) and Mountford (1990) are the only ones that come to mind. For a cen- tury or so – since the realization that unwritten languages are as legitimate a field of study, and perhaps a more important one, than the world’s handful of literary languages – writing systems were (rightly) seen as secondary to phonological systems and (wrongly) set aside as unworthy of study or at best irrelevant to spoken language. The one exception was I. J. Gelb’s attempt (1952, reissued with additions and corrections 1963) to create a theory of writ- ing informed by the linguistics of his time. Gelb said that what he wrote was meant to be the first word, not the last word, on the subject, but no successors appeared until after his death in 1985.1 Although there have been few lin- guistic explorations of writing, a number of encyclopedic compilations have appeared, concerned largely with the historical development and diffusion of writing,2 though various popularizations, both new and old, tend to be less than accurate (Daniels 2000). Daniels and Bright (1996; The World’s Writing Systems: hereafter WWS) includes theoretical and historical materials but is primarily descriptive, providing for most contemporary and some earlier scripts information (not previously gathered together) on how they represent (the sounds of) the languages they record. This chapter begins with a historical-descriptive survey of the world’s writ- ing systems, and elements of a theory of writing follow.
    [Show full text]
  • Multi-Class Support Vector Machine Based Continuous Voiced Odia Numerals Recognition
    INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 10, OCTOBER 2019 ISSN 2277-8616 Multi-Class Support Vector Machine Based Continuous Voiced Odia Numerals Recognition Prithviraj Mohanty, Ajit Kumar Nayak Abstract: With rapid advancement of automatic speech-recognition technologies, speech-based machine interaction has fascinated attention by many researchers to apply their approach from the research laboratory to real-life applications. A continuous voiced numerals recognition system is always useful for physically challenged persons (blind people) or elder people to have a telephonic conversion, setting the PIN number for their debit and credit cards and also devising the security code for some applications without physically touching the system. The work presented on this paper emphasize the recognition of continuous Odia numerals using multi-class Support Vector Machine (SVM).Three popular feature extraction techniques such as: PLP, LPC and MFCC are used to extract the feature parameters from voiced numerals and fed as the input to the recognition process. Different kernel mapping functions like: polynomial, sigmoid, Radial Basis Function (RBF) and wavelet are used in order to map the non-linear input feature space of framed signals to linear high dimensional feature space. So as to recognize the Odia numerals, multi-class SVM models are constructed using the techniques of One-Verses-All (OVA) and Half-Verses-Half (HVH). For the proposed system, diverse experimentations has been performed and results are analyzed over multi-class SVM models considering different feature parameter techniques with various kernel mapping functions. It has been observed that, OVA SVM model with MFCC for feature extraction and wavelet as kernel mapping function provides better accuracy as compared to other variations of the results attained.
    [Show full text]
  • Dr. Babita Majhi Asst
    Dr. Babita Majhi Asst. Professor Dept. of Computer Science and Information Technology G. G. Vishwavidyalaya (A Central University), Bilaspur-495009(CG),India Email:[email protected], Mobile :9098715203 Academic Appointments: _______________________________________________________________________ 13/01/2012 –Till date: Asst. Professor, Dept. of CSIT, Guru Ghasidas Vishwavidyalaya (Central University), Bilaspur, India (Dec. 2011-Dec. 2012) Boyscast Fellow(DST, GOI) at University of Sheffield, United Kingdom. 2009-2011 : Asst. Professor, Dept. of CSIT, Institute of Technical Education and Research, Siksha “O” Anusandhan (Deemed to be) University, Bhubaneswar, India.(NIRF Ranking 2019 : 24) ____________________________________________________________________________ Education : Post Doc.(Boyscast Fellow, DST, GOI) Dec.2011-Dec.2012, University of Sheffield, United Kingdom (UK). (QS Global World ranking 2019 : 78) Ph.D., 2009, National Institute of Technology Rourkela, Odisha, India. (NIRF Ranking 2019: 16). Master of Computer Application(MCA),Sambalpur University/Biju Patnaik University of Technology, Odisha, India. ______________________________________________________________________ Research Experience : _________________________________________________________________________ Ph. D. Thesis : On Applications of Soft and Evolutionary Computing Techniques to Direct and Inverse modeling Problems Supervisor : Prof. G. Panda,FNAE, FNASc., Indian Institute of Technology, Bhubaneswar Post Doctoral work for one year under BOYSCAST Fellowship
    [Show full text]
  • INDO-EUROPEAN NUMERALS INDO-EUROPEAN "One" and "First"
    141 INDO-EUROPEAN NUMERALS INDO-EUROPEAN "one" and "first" For Eva Havlová, the first lady of Czech etymology §1. In Indo-European languages the numeral "one" was formed from one of two roots: A. *oy-, B. *sem-. The ordinal "first", usually derived from the root *per-/*pro-, meant originally "fóre, foremost". The most important forms can be projected in the following partial reconstructions allowing their deeper analysis. Indo-Iranian: A. *oy- *oy-ko- "1" > Indo-Aryan *aika- > Mitanni Aryan aika- in a-i-ka-wa-ar-ta- na "one tum" (Puhvel 1984: 14), Old Indie éka-, Pali eka-, ekka-, Prakrit ikka, igga, ěa, Hindi ek, Oriya eka besides e (cf. Prakrit ěa), Sindhi eku etc, Kashmiri akh, Shina ik, Phalura ak, Torwali ek, ě, Tirahi, Kalasha, Dameli ek, Bashkarik, Maiyan ak, Shumashti yak, Wotapuri yek, Gawar yak, Waigali ěk, ew. Kati ev, Ashkun ač, Pashai, Khowar I (cf. Prakrit ěa). Let us mention that the alternativě reconstruction *oy-kwo- (Waanders 1992: 370) is also quite legitimate. *oy-wo- "1" > Iranian *aiwa- > Avestan aěuua-, Khwarezmian ěw, Sog- dian (Buddhistic) 'yw, (Manichean) lyw, (Christian) yw, Yaghnobi i, Pashto yau, Yidgha, Mundjan, Ishkashim yu, Wakhi lu, Shugni yl(w), Sarikoli T(w), Ossetic Digor ew, Iron íw, Old Persian aiva-, Zoroastrian Pahlavi ěv, ěvak (*aivaka, cf. Modern Persian yak "1" and yekom "lst", already in Zoroastrian Pahlavi ěkom "lst", Baluchi ěyók "single", evak'a "alone", Sanglechi wok, yak, Yazgulam yů(g) "1" and further Khotanese ššúka- "alone", Parthian 'wg id.), Modern Persian dial. yaw, Zaza yau, Talysh i; cf. also Old Indie adv.
    [Show full text]
  • Chapter 22, Symbols
    The Unicode® Standard Version 13.0 – Core Specification To learn about the latest version of the Unicode Standard, see http://www.unicode.org/versions/latest/. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trade- mark claim, the designations have been printed with initial capital letters or in all capitals. Unicode and the Unicode Logo are registered trademarks of Unicode, Inc., in the United States and other countries. The authors and publisher have taken care in the preparation of this specification, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein. The Unicode Character Database and other files are provided as-is by Unicode, Inc. No claims are made as to fitness for any particular purpose. No warranties of any kind are expressed or implied. The recipient agrees to determine applicability of information provided. © 2020 Unicode, Inc. All rights reserved. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction. For information regarding permissions, inquire at http://www.unicode.org/reporting.html. For information about the Unicode terms of use, please see http://www.unicode.org/copyright.html. The Unicode Standard / the Unicode Consortium; edited by the Unicode Consortium. — Version 13.0. Includes index. ISBN 978-1-936213-26-9 (http://www.unicode.org/versions/Unicode13.0.0/) 1.
    [Show full text]
  • Feature Extraction and Dimensionality Reduction in Pattern Recognition Using Handwritten Odia Numerals
    Middle-East Journal of Scientific Research 22 (10): 1514-1519, 2014 ISSN 1990-9233 © IDOSI Publications, 2014 DOI: 10.5829/idosi.mejsr.2014.22.10.21487 Feature Extraction and Dimensionality Reduction in Pattern Recognition Using Handwritten Odia Numerals 12Pradeepta K. Sarangi and Kiran K. Ravulakollu 1Apeejay Institute of Technology, Greater Noida, U.P, India 2Sharda University, Greater Noida, U.P, India Abstract: Feature extraction is the initial and critical stage that needs to be carried out for any recognition system that uses pattern matching. Many of the existing feature extraction techniques such as Quadrant-mean, Histogram and Texture extraction are based on the principle of partitioning the image into small segments. However, as the size of the image increases, the complexity of computation also increases. When it comes to image reconstruction from the feature values, these methods are not successful in generation a complete and valid original image. In addition, as the number of components in the image increases, the dimensionality of the features also increases. In order to reduce the feature extraction complexity, dimensionality reduction is applied to increase the recognition performance. To perform image restoration, transformation of limited feature vector to binary image is considered. In this paper, dimensionality reduction and feature extraction is achieved using row-wise decimal conversion for pattern recognition. This signifies the transformation of features (small number) to a normalized state thereby preserving quality of image (numeral). A simple recurrent neural network (RNN) has been used as the classifier and recognition accuracy is reported. The method has been implemented on the dataset consisting of 1500-isolated handwritten Odia numerals demonstrating an accuracy of 92.4%.
    [Show full text]
  • Application of Cat Swarm Optimization for Recognition of Handwritten Numerals
    International Journal of Computer Sciences and Engineering Open Access Research Paper Vol.7, Special Issue.3, Feb. 2019 E-ISSN: 2347-2693 Application of Cat Swarm Optimization for Recognition of Handwritten Numerals Puspalata Pujari1*, Babita Majhi2 1,2Department of C.S.I.T Guru Ghasidas Vishwavidyalaya, Bilaspur, India *Corresponding Author: [email protected] Available online at: www.ijcseonline.org Abstract—Accurate recognition of optical handwritten numeral is still an open and demanding problem in present digital world. The basic objective of the present work is to develop a novel method for recognition of off line unconstrained handwritten Odia numeral using curvature feature and functional link artificial neural network (FLANN) based cat swarm optimization (CSO) technique. In this paper preprocessing and feature extraction steps are carried out before the recognition of numerals. For feature extraction curvature based approach is applied. For recognition of handwritten Odia numeral hybrid architecture has been proposed where the classification task is performed by FLANN classifier and cat swarm optimization is used for finding a suitable set of weights for the FLANN classifier. The proposed model is evaluated on database consisting of 4000 number of handwritten Odia numerals. The combined effect of curvature based feature extraction approach and FLANN based cat swarm optimization technique yielded a high accuracy which exhibits the effectiveness of CSO based FLANN optimization model (FLANN-CSO) for recognition of Odia handwritten numerals. Keywords— Odia numeral recognition, FLANN, CSO, Preprocessing, Feature extraction, classification. I. INTRODUCTION Recognition accuracy of 97.99 % and 94.35 % is achieved with HMM and NNC respectively. A novel model has been Recognition of unconstrained handwritten numerals has proposed in [4] for the recognition of Persian / Arabic significant effect in various applications related to postal handwritten zip code.
    [Show full text]