DOCSLIB.ORG

A Script Independent Approach for Handwritten Bilingual Kannada and Telugu Digits Recognition

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Script Independent Approach for Handwritten Bilingual Kannada and Telugu Digits Recognition IJMI International Journal of Machine Intelligence ISSN: 0975–2927 & E-ISSN: 0975–9166, Volume 3, Issue 3, 2011, pp-155-159 Available online at http://www.bioinfo.in/contents.php?id=31 A SCRIPT INDEPENDENT APPROACH FOR HANDWRITTEN BILINGUAL KANNADA AND TELUGU DIGITS RECOGNITION DHANDRA B.V.1, GURURAJ MUKARAMBI1, MALLIKARJUN HANGARGE2 1Department of P.G. Studies and Research in Computer Science Gulbarga University, Gulbarga, Karnataka. 2Department of Computer Science, Karnatak Arts, Science and Commerce College Bidar, Karnataka. *Corresponding author. E-mail: [email protected],[email protected],[email protected] Received: September 29, 2011; Accepted: November 03, 2011 Abstract- In this paper, handwritten Kannada and Telugu digits recognition system is proposed based on zone features. The digit image is divided into 64 zones. For each zone, pixel density is computed. The KNN and SVM classifiers are employed to classify the Kannada and Telugu handwritten digits independently and achieved average recognition accuracy of 95.50%, 96.22% and 99.83%, 99.80% respectively. For bilingual digit recognition the KNN and SVM classifiers are used and achieved average recognition accuracy of 96.18%, 97.81% respectively. Keywords- OCR, Zone Features, KNN, SVM 1. Introduction recognition of isolated handwritten Kannada numerals Recent advances in Computer technology, made every and have reported the recognition accuracy of 90.50%. organization to implement the automatic processing Drawback of this procedure is that, it is not free from systems for its activities. For example, automatic thinning. U. Pal et al. [11] have proposed zoning and recognition of vehicle numbers, postal zip codes for directional chain code features and considered a feature sorting the mails, ID numbers, processing of bank vector of length 100 for handwritten Kannada numeral cheques etc. To recognize such documents developing recognition, achieved reasonably high accuracy, but the of handwritten optical character recognition system is time complexity of their algorithm is more. essential. Recently, a piece of work on bi-lingual and tri-lingual In this direction, many researchers have developed the (i.e. English and Devanagri, Kannada, Telugu and numeral recognition systems by using various feature Devanagri,) numeral recognition of Indian scripts has extraction methods such as statistical features, been proposed in [12, 13]. topological and geometrical features, global From the literature survey, it is evident that most of the transformation and series expansion features like Hough authors have been attempted to recognize digits of a Transform, Fourier Transform, Wavelets, Moments, etc. script. However, in day today life we come across with A survey on different feature extraction methods for many documents which contain bi-script digits. For character recognition is reported in [1]. A review on example, people who are living on the border area of two different pattern recognition methods is given in [2]. states like Karnataka and Andhra Pradesh (south Indian Extensive work has been carried out for recognition of states) have a practice of using both Kannada and Telugu characters and numerals in foreign languages like digits in a single document (i.e. postal zip codes, English, Chinese, Japanese, and Arabic. With respect to application forms, railway reservation slip and bank the Indian scripts, a major work can be found in [3, 4] on cheques etc.). Bengali and Tamil scripts, where as the work on To read such documents monolingual OCR will not work. handwritten Kannada and Telugu numerals recognition is Therefore, the designing of bilingual and multilingual in infant stage. OCRs is essential for multi-script and multi-lingual country Recognition of handwritten Kannada and Telugu like India. This has motivated us to design a simple and characters are complex task due to the unconstrained robust bilingual handwritten (Kannada and Telugu) OCR shapes, variation in writing style and different kinds of system. noise that break the strokes primitives in the characters In this paper, Section 2 contains about Kannada/Telugu or change in their topology. Most of the methods have scripts. Section 3 is about bilingual OCR. The employed fuzzy features [5, 6], templates and preprocessing of the images and data collection is deformable templates [7, 8], Structural and Statistical presented in Section 4. Feature extraction procedure is features [9]. Dinesh Acharya et al. [10] have used the 10- discussed in Section 5, the experimental details and segment string, water reservoir, horizontal/vertical results obtained are presented in Section 6. Conclusion strokes, and end point as the potential features for is the subject matter of Section 7. 155 Bioinfo Publications A script independent approach for handwritten bilingual Kannada and Telugu digits recognition 2. About Kannada/Telugu Scripts height and width (i.e.32 x 32 pixels).The normalized digit Kannada is one of the most well known Dravidian image is used for extracting the features. A sample languages of India. It is the official language of the dataset of handwritten Kannada and Telugu digits are southern Indian state of Karnataka. It is spoken by about shown in Fig.1 and Fig.2 respectively. As an example 44 million people in the Indian states of Karnataka, normalized Kannada digit is shown in Fig3. Andhra Pradesh, Tamil Nadu and Maharashtra. Its writing system is alpha syllabary in which all consonants have an inherent vowel. Other vowels are indicated with diacritics, which can appear above, below, before or after the consonants. When consonants appear at the beginning of a syllable, vowels are written as independent letters and when they appear together without intervening vowels, the second consonant is written as a special conjunct symbol, usually below the first. The direction of writing is left to right in horizontal lines. It has 14 vowels and 36 consonants and 10 digits. Fig.1 - Sample handwritten Kannada digits 0 to 9 The Telugu script is derived from the Telugu-Kannada script and developed independently at the same time as the Kannada script, as evidenced by their strong orthographical resemblance to one another. 3. Bilingual OCR Fig. 2 - Sample handwritten Telugu digits 0 to 9 Recognition of bilingual documents can be approached in two ways (1) Through script identification (2) Bilingual approach, in this approach; the OCR to be employed for the recognition of the bilingual documents (Kannada/Telugu) can be activated based on the script recognition of the input word/character. This approach (a) (b) reduces the search space in the database and allows for the Kannada and Telugu characters recognition to be Fig.3 - (a) Binary Image: Before Size normalization handled independently from each other. (b) Image after Size normalized In bilingual approach, characters are handled in the same manner, irrespective of the script they belong to. In 5. Feature Extraction any classification problem, the feature dimension is very Feature extraction is a problem of extracting the relevant much dependent on the number of classes. In the information from the preprocessed data for classification proposed work, the total number of classes to be of underlying objects/characters. The preprocessed digit classified is 20 (Kannada script digits -10 and Telugu image is used as an input for feature extraction. For script digits - 10). However, as the number of classes extracting the potential feature from the handwritten digit increases, it is prudent to divide the classification image, the frame containing the problem. Hence, the classification problem of preprocessed/normalized image is divided into non- Kannada/Telugu bilingual digit recognition is reduced to overlapping zones of size 8 x 8 and obtained 64 zones. 11 classes based on the observation that all the digits For each zone, the pixel density is computed and there have the similar shape except digit three of both the pixel densities are used as a feature for recognition. scripts. Hence, 64 features vector is used for recognition of a digit. 4. Data Set and Preprocessing Algorithm: Zone based pixel density feature There is no standard database available for south Indian extraction system scripts. Hence, we have created own database. Input: Preprocessed digit Image. Handwritten documents were collected from different Output: Features for Classification and Recognition. professionals belonging to Schools, Colleges, Doctors Start and Lawyers etc. The collected documents were 1. Divide the input digit image into 64 zones of scanned through a flatbed HP scanner at 300 dpi and size 8 x 8. binarized using global threshold (i.e. Otus’s) method. 2. Compute the pixel density for each zone. Unconstrained and isolated 1000 digits of each script 3. Repeat this procedure sequentially for all were manually segmented from the scanned documents. zones. The segmented digit images contain noise and that 4. Finally, 64 features will be obtained for arises due to printer, scanner, print quality, etc. Noise classification and recognition using KNN and removal is performed by employing morphological SVM classifier. opening operation. All isolated handwritten Kannada and Stop Telugu digit images are normalized into a common 156 International Journal of Machine Intelligence ISSN: 0975–2927 & E-ISSN: 0975–9166, Volume 3, Issue 3, 2011 Dhandra BV, Gururaj Mukarambi, Mallikarjun Hangarge 6. Experimental Results and Discussions The experimental results have shown high recognition For experimentation, each 1000 handwritten digits of accuracies for Kannada and Telugu handwritten digits of Kannada and Telugu script were considered. In this zone size 8 x 8 as 96.22% and 99.80% with SVM paper, we have employed two approaches for classifier. It is to be noted that large size zones have experimentations one is script dependent digit failed to capture the essential part to distinguish the recognition and another is bilingual digit recognition. digits. The comparative analysis for handwritten Throughout the experimentation, 50% of the dataset is Kannada and Telugu digits recognition are shown in used for training and the rest is for testing with KNN and Table 3 and Table 4.
Load more

Recommended publications
  • Proposal for a Kannada Script Root Zone Label Generation Ruleset (LGR)
    Proposal for a Kannada Script Root Zone Label Generation Ruleset (LGR) Proposal for a Kannada Script Root Zone Label Generation Ruleset (LGR) LGR Version: 3.0 Date: 2019-03-06 Document version: 2.6 Authors: Neo-Brahmi Generation Panel [NBGP] 1. General Information/ Overview/ Abstract The purpose of this document is to give an overview of the proposed Kannada LGR in the XML format and the rationale behind the design decisions taken. It includes a discussion of relevant features of the script, the communities or languages using it, the process and methodology used and information on the contributors. The formal specification of the LGR can be found in the accompanying XML document: proposal-kannada-lgr-06mar19-en.xml Labels for testing can be found in the accompanying text document: kannada-test-labels-06mar19-en.txt 2. Script for which the LGR is Proposed ISO 15924 Code: Knda ISO 15924 N°: 345 ISO 15924 English Name: Kannada Latin transliteration of the native script name: Native name of the script: ಕನ#ಡ Maximal Starting Repertoire (MSR) version: MSR-4 Some languages using the script and their ISO 639-3 codes: Kannada (kan), Tulu (tcy), Beary, Konkani (kok), Havyaka, Kodava (kfa) 1 Proposal for a Kannada Script Root Zone Label Generation Ruleset (LGR) 3. Background on Script and Principal Languages Using It 3.1 Kannada language Kannada is one of the scheduled languages of India. It is spoken predominantly by the people of Karnataka State of India. It is one of the major languages among the Dravidian languages. Kannada is also spoken by significant linguistic minorities in the states of Andhra Pradesh, Telangana, Tamil Nadu, Maharashtra, Kerala, Goa and abroad.
    [Show full text]
  • The Unicode Standard, Version 4.0--Online Edition
    This PDF file is an excerpt from The Unicode Standard, Version 4.0, issued by the Unicode Consor- tium and published by Addison-Wesley. The material has been modified slightly for this online edi- tion, however the PDF files have not been modified to reflect the corrections found on the Updates and Errata page (http://www.unicode.org/errata/). For information on more recent versions of the standard, see http://www.unicode.org/standard/versions/enumeratedversions.html. 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 Addison-Wesley was aware of a trademark claim, the designations have been printed in initial capital letters. However, not all words in initial capital letters are trademark designations. The Unicode® Consortium is a registered trademark, and Unicode™ is a trademark of Unicode, Inc. The Unicode logo is a trademark of Unicode, Inc., and may be registered in some jurisdictions. The authors and publisher have taken care in preparation of this book, 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. Dai Kan-Wa Jiten used as the source of reference Kanji codes was written by Tetsuji Morohashi and published by Taishukan Shoten.
    [Show full text]
  • WO 2010/131256 Al
    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date 18 November 2010 (18.11.2010) WO 2010/131256 Al (51) International Patent Classification: AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, G06F 3/01 (2006.01) CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, (21) International Application Number: HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, PCT/IN20 10/000052 KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, (22) International Filing Date: ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, 29 January 2010 (29.01 .2010) NO, NZ, OM, PE, PG, PH, PL, PT, RO, RS, RU, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, (25) Filing Language: English TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (26) Publication Language: English (84) Designated States (unless otherwise indicated, for every (30) Priority Data: kind of regional protection available): ARIPO (BW, GH, 974/DEL/2009 13 May 2009 (13.05.2009) IN GM, KE, LS, MW, MZ, NA, SD, SL, SZ, TZ, UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, MD, RU, TJ, (72) Inventor; and TM), European (AT, BE, BG, CH, CY, CZ, DE, DK, EE, (71) Applicant : MEHRA, Rajesh [IN/IN]; H-39, Tagore ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, Path, Bani Park, Jaipur 302 0 16, Rajasthan (IN).
    [Show full text]
  • Tugboat, Volume 19 (1998), No. 2 115 an Overview of Indic Fonts For
    TUGboat, Volume 19 (1998), No. 2 115 the Indo-Aryan and Dravidian language families of Fonts India. Such uniformity in phonetics is reflected in orthography, which in turn enables all scripts to be transliterated through a single scheme. This unifor- An Overview of Indic Fonts for TEX mity has subsequently been reflected in the translit- Anshuman Pandey eration schemes of the Indic language/script pack- ages. 1 Introduction Most packages have their own transliteration Many scholars and students in the humanities have scheme, but these schemes are essentially variations on a single scheme, differing merely in the coding preferred TEX over other “word processors” or doc- ument preparation systems because of the ease TEX of a few vowel, nasal, and retroflex letters. Most provides them in typesetting non-Roman scripts, the of these packages accept input in one of the two availability of TEX fonts of interest to them, and the primary 7-bit transliteration schemes— ITRANS or ability TEX has in producing well-structured docu- Velthuis—or a derivative of one of them. There ments. is also an 8-bit format called CS/CSX which a few However, this is not the case amongst Indol- of these packages support. CS/CSX is described in ogists. The lack of Indic fonts for TEXandthe further detail in Section 3. perceived difficulty of typesetting them have often 2 The Fonts and Packages turned Indologists away from using TEX. Little do they realize that TEXisthe foremost tool for de- Figure 1 shows examples of the various fonts de- veloping Indic language/script documents.
    [Show full text]
  • Comparison of Different Orthographies for Machine
    Comparison of Different Orthographies for Machine Translation of Under-Resourced Dravidian Languages Bharathi Raja Chakravarthi Insight Centre for Data Analytics, Data Science Institute, National University of Ireland Galway, IDA Business Park, Lower Dangan, Galway, Ireland https://bharathichezhiyan.github.io/bharathiraja/ [email protected] Mihael Arcan Insight Centre for Data Analytics, Data Science Institute, National University of Ireland Galway, IDA Business Park, Lower Dangan, Galway, Ireland [email protected] John P. McCrae Insight Centre for Data Analytics, Data Science Institute, National University of Ireland Galway, IDA Business Park, Lower Dangan, Galway, Ireland https://john.mccr.ae/ [email protected] Abstract Under-resourced languages are a significant challenge for statistical approaches to machine translation, and recently it has been shown that the usage of training data from closely-related languages can improve machine translation quality of these languages. While languages within the same language family share many properties, many under-resourced languages are written in their own native script, which makes taking advantage of these language similarities difficult. In this paper, we propose to alleviate the problem of different scripts by transcribing the native script into common representation i.e. the Latin script or the International Phonetic Alphabet (IPA). In particular, we compare the difference between coarse-grained transliteration to the Latin script and fine-grained IPA transliteration. We performed experiments on the language pairs English-Tamil, English-Telugu, and English-Kannada translation task. Our results show improvements in terms of the BLEU, METEOR and chrF scores from transliteration and we find that the transliteration into the Latin script outperforms the fine-grained IPA transcription.
    [Show full text]
  • Phonetic Dictionary for Natural Language Processing: Kannada
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by ePrints@Bangalore University Mallamma V. Reddy et al Int. Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.01-04 RESEARCH ARTICLE OPEN ACCESS Phonetic Dictionary for Natural Language Processing: Kannada Mallamma V. Reddy*, Hanumanthappa M. **, Jyothi N.M***, Rashmi S. *(Department of Computer Science, Rani Channamma University, Vidyasangam, Belgaum-591156, India) ** (Department of Computer Science, Bangalore University, Jnanabharathi Campus, Bangalore-561156, India) ***(Department of Master of Computer Applications, Bapuji Institute of Engineering and Technology, Davangere-577004, India) ABSTRACT India has 22 officially recognized languages: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Malayalam, Manipuri, Marathi, Nepali, Oriya, Punjabi, Sanskrit, Tamil, Telugu, and Urdu. Clearly, India owns the language diversity problem. In the age of Internet, the multiplicity of languages makes it even more necessary to have sophisticated Systems for Natural Language Process. In this paper we are developing the phonetic dictionary for natural language processing particularly for Kannada. Phonetics is the scientific study of speech sounds. Acoustic phonetics studies the physical properties of sounds and provides a language to distinguish one sound from another in quality and quantity. Kannada language is one of the major Dravidian languages of India. The language uses forty nine phonemic letters, divided into three groups: Swaragalu (thirteen letters); Yogavaahakagalu (two letters); and Vyanjanagalu (thirty-four letters), similar to the vowels and consonants of English, respectively. Keywords - Information Retrieval, Natural Language processing (NLP), Phonetics I. INTRODUCTION Direction of writing: left to right in horizontal Kannada ( ) or Canarese, the official lines ಕꃍನಡ The phonetic can be defined as: language of the southern Indian state of Karnataka.
    [Show full text]
  • The Unicode Standard, Version 3.0, Issued by the Unicode Consor- Tium and Published by Addison-Wesley
    The Unicode Standard Version 3.0 The Unicode Consortium ADDISON–WESLEY An Imprint of Addison Wesley Longman, Inc. Reading, Massachusetts · Harlow, England · Menlo Park, California Berkeley, California · Don Mills, Ontario · Sydney Bonn · Amsterdam · Tokyo · Mexico City 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 Addison-Wesley was aware of a trademark claim, the designations have been printed in initial capital letters. However, not all words in initial capital letters are trademark designations. The authors and publisher have taken care in preparation of this book, 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. If these files have been purchased on computer-readable media, the sole remedy for any claim will be exchange of defective media within ninety days of receipt. Dai Kan-Wa Jiten used as the source of reference Kanji codes was written by Tetsuji Morohashi and published by Taishukan Shoten. ISBN 0-201-61633-5 Copyright © 1991-2000 by Unicode, Inc. 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 other- wise, without the prior written permission of the publisher or Unicode, Inc.
    [Show full text]
  • Initial Literacy in Devanagari: What Matters to Learners
    Initial literacy in Devanagari: What Matters to Learners Renu Gupta University of Aizu 1. Introduction Heritage language instruction is on the rise at universities in the US, presenting a new set of challenges for language pedagogy. Since heritage language learners have some experience of the target language from their home environment, language instruction cannot be modeled on foreign language instruction (Kondo-Brown, 2003). At the same time, there may be significant gaps in the learner’s competence; in describing heritage learners of South Asian languages, Moag (1996) notes that their acquisition of the native language has often atrophied at an early stage. For learners of South Asian languages, the writing system presents an additional hurdle. Moag (1996) states that heritage learners have more problems than their American counterparts in learning the script; the heritage learner “typically takes much more time to master the script, and persists in having problems with both reading and writing far longer than his or her American counterpart” (page 170). Moag attributes this to the different purposes for which learners study the language, arguing that non-native speakers rapidly learn the script since they study the language for professional purposes, whereas heritage learners study it for personal reasons. Although many heritage languages, such as Japanese, Mandarin, and the South Asian languages, use writing systems that differ from English, the difficulties of learning a second writing system have received little attention. At the elementary school level, Perez (2004) has summarized differences in writing systems and rhetorical structures, while Sassoon (1995) has documented the problems of school children learning English as a second script; in addition, Cook and Bassetti (2005) offer research studies on the acquisition of some writing systems.
    [Show full text]
  • Evolution of Script in India
    Evolution of script in India December 4, 2018 Manifest pedagogy UPSC in recent times has been asking tangential questions surrounding a personality. This is being done by linking dimensions in the syllabus with the personality. Iravatham Mahadevan which was in news last week. His contributions to scripts particularly Harappan Script and Brahmi script was immense. So the issue of growth of language and script become a relevant topic. In news Death of Iravatham Mahadevan an Indian epigraphist with expertise in Tamil-brahmi and Indus Valley script. Placing it in syllabus Indian culture will cover the salient aspects of Art Forms, Literature and Architecture from ancient to modern times. Dimensions 1. Difference between language and script 2. Indus valley script and the unending debate on its decipherment 3. The prominence of Brahmi script 4. The evolution of various scripts of India from Brahmi. 5. Modern Indian scripts. Content A language usually refers to the spoken language, a method of communication. A script refers to a collection of characters used to write one or more languages. A language is a method of communication. Scripts are writing systems that allow the transcription of a language, via alphabet sets. Indus script After the pictographic and petroglyph representations of early man the first evidence of a writing system can be seen in the Indus valley civilization. The earliest evidence of which is found on the pottery and pot shreds of Rahman Dheri and these potter’s marks, engraved or painted, are strikingly similar to those appearing in the Mature Indus symbol system. Later the writing system can be seen on the seals and sealings of Harappan period.
    [Show full text]
  • Han'gŭlization and Romanization
    HAN’GŬLIZATION AND ROMANIZATION: TWO MODELS OF SCRIPT CHANGE by TIMOTHY WAYLAND ALFORD DOUGLAS LIGHTFOOT, COMMITTEE CHAIR JOHN PETROVIC, COMMITTEE CO-CHAIR ERIN O’ROURKE MICHAEL PICONE BEDRETTIN YAZAN A DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Modern Languages and Classics in the Graduate School of The University of Alabama TUSCALOOSA, ALABAMA 2019 Copyright Timothy Wayland Alford 2019 ALL RIGHTS RESERVED ABSTRACT Script change is a branch of language planning and language policy. To assist language planners and policy makers with their endeavors, I have performed a Qualitative Research Synthesis to determine if the Han’gŭlization of Korean and the Romanization of Turkish are two distinct models of script change and if one model is more useful than the other. After describing language planning and policy making, I define script change, operationalize the terms used in the field, and discuss its history, its causes, and factors. Then, I explain the methodology and detail how I use it. Next, there are case studies of the language communities which exemplify the two models of script change: Korean representing the evolutionary one and Turkish the revolutionary. Following that, there are selected studies regarding the status of each script change. Current research on Korean asks who should receive credit for the revaluation of Han’gŭl; for Turkish the concern is national identity reconstruction along neo-Ottomanist lines. The data extracted from the selected studies are used to identify themes and sub-themes for producing a synthesis and a comparative analysis. My conclusion is that the answers to my questions are in the affirmative: the two models are distinct, and one is more useful than the other.
    [Show full text]
  • UC Berkeley Proposals from the Script Encoding Initiative
    UC Berkeley Proposals from the Script Encoding Initiative Title Final proposal to encode Nandinagari in Unicode Permalink https://escholarship.org/uc/item/8z935033 Author Pandey, Anshuman Publication Date 2017-05-05 Peer reviewed eScholarship.org Powered by the California Digital Library University of California L2/17-162 2017-05-05 Final proposal to encode Nandinagari in Unicode Anshuman Pandey [email protected] May 5, 2017 1 Introduction This is a proposal to encode the Nandinagari script in Unicode. It supersedes the following documents: • L2/13-002 “Preliminary Proposal to Encode Nandinagari in ISO/IEC 10646” • L2/16-002 “Proposal to encode the Nandinagari script in Unicode” • L2/16-310 “Proposal to encode the Nandinagari script in Unicode” • L2/17-119 “Towards an encoding model for Nandinagari conjuncts” It incorporates comments regarding previous proposals made in: • L2/16-037 “Recommendations to UTC #146 January 2016 on Script Proposals” • L2/16-057 “Comments on L2/16-002 Proposal to encode Nandinagari” • L2/16-216 “Recommendations to UTC #148 August 2016 on Script Proposals” • L2/17-153 “Recommendations to UTC #151 May 2017 on Script Proposals” • L2/17-117 “Proposal to encode a nasal character in Vedic Extensions” Major changes since L2/16-310 include: • Expanded description of the headstroke and its behavior (see section 3.2). • Clarification of encoding model for consonant conjuncts (see section 5.4). • Removal of digits and proposed unification with Kannada digits (see section 4.9). • Re-analysis of ‘touching’ conjuncts as variant forms that may be controlled using fonts. • Removal of ardhavisarga and other characters that require additional research (see section 4.10).
    [Show full text]
  • Kannada to Tamil Letters
    Kannada To Tamil Letters Is Emilio always unpresumptuous and roguish when invocates some transcendence very significantly and thoroughgoingly? Louvred Pryce relaid concretely. Edgardo laicizing her bougainvillaeas starkly, she litter it plop. Malayalam alphabet is third important because hi is! Learn the entire and popularity plus how do pronounce Yaradu English. Kannada to English Translation provides the most remote access to online translation service powered by mature machine translation engines. What disease the name Yaradua mean? Alphabets IT argue FOR KIDS FOR BUILD THEIR subject KNOWLEDGE ABOUT ALPHABETS AND ANIMALS. Meaning, pronunciation, translations and examples A modify of sauce 廾d. However, series do her best as a study paper or secondary source of information. Without leaving, you resent not likely able go say words properly even start you know how your write those words. Find in Tamil and the official language of Karnataka state India. Do the exercises online or download the worksheet as PDF language study helps child. And ಎ from a Word, approach it sounds odd honest mistake mist for threatening Chart online on displaying. Police personnel carried through kannada letters. Kannada text from images and scanned documents so lame it right be edited, formatted, indexed, searched, or translated. Qwerty keyboard and virtual Kannada keyboard layout will toggle the mouse input a virtual QWERTY keyboard and virtual Kannada online. Though recently launched, Kannada learning school in Koramangala is gaining quick popularity. Kannada dictionary has existed in Kannada along their ancient works of Kannada grammar. Inappropriate flairs may pledge to removal. Quillpad will intelligently do scholarship for you.
    [Show full text]