A Hybrid of Sinhala and Tamil Scripts for Sri Lanka
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Cross-Language Framework for Word Recognition and Spotting of Indic Scripts
Cross-language Framework for Word Recognition and Spotting of Indic Scripts aAyan Kumar Bhunia, bPartha Pratim Roy*, aAkash Mohta, cUmapada Pal aDept. of ECE, Institute of Engineering & Management, Kolkata, India bDept. of CSE, Indian Institute of Technology Roorkee India cCVPR Unit, Indian Statistical Institute, Kolkata, India bemail: [email protected], TEL: +91-1332-284816 Abstract Handwritten word recognition and spotting of low-resource scripts are difficult as sufficient training data is not available and it is often expensive for collecting data of such scripts. This paper presents a novel cross language platform for handwritten word recognition and spotting for such low-resource scripts where training is performed with a sufficiently large dataset of an available script (considered as source script) and testing is done on other scripts (considered as target script). Training with one source script and testing with another script to have a reasonable result is not easy in handwriting domain due to the complex nature of handwriting variability among scripts. Also it is difficult in mapping between source and target characters when they appear in cursive word images. The proposed Indic cross language framework exploits a large resource of dataset for training and uses it for recognizing and spotting text of other target scripts where sufficient amount of training data is not available. Since, Indic scripts are mostly written in 3 zones, namely, upper, middle and lower, we employ zone-wise character (or component) mapping for efficient learning purpose. The performance of our cross- language framework depends on the extent of similarity between the source and target scripts. -
SC22/WG20 N896 L2/01-476 Universal Multiple-Octet Coded Character Set International Organization for Standardization Organisation Internationale De Normalisation
SC22/WG20 N896 L2/01-476 Universal multiple-octet coded character set International organization for standardization Organisation internationale de normalisation Title: Ordering rules for Khmer Source: Kent Karlsson Date: 2001-12-19 Status: Expert contribution Document type: Working group document Action: For consideration by the UTC and JTC 1/SC 22/WG 20 1 Introduction The Khmer script in Unicode/10646 uses conjoining characters, just like the Indic scripts and Hangul. An alternative that is suitable (and much more elegant) for Khmer, but not for Indic scripts, would have been to have combining- below (and sometimes a bit to the side) consonants and combining-below independent vowels, similar to the combining-above Latin letters recently encoded, as well as how Tibetan is handled. However that is not the chosen solution, which instead uses a combining character (COENG) that makes characters conjoin (glue) like it is done for Indic (Brahmic) scripts and like Hangul Jamo, though the latter does not have a separate gluer character. In the Khmer script, using the COENG based approach, the words are formed from orthographic syllables, where an orthographic syllable has the following structure [add ligation control?]: Khmer-syllable ::= (K H)* K M* where K is a Khmer consonant (most with an inherent vowel that is pronounced only if there is no consonant, independent vowel, or dependent vowel following it in the orthographic syllable) or a Khmer independent vowel, H is the invisible Khmer conjoint former COENG, M is a combining character (including COENG, though that would be a misspelling), in particular a combining Khmer vowel (noted A below) or modifier sign. -
Constructed Scripts
The Art and Science of Constructed Scripts Jasper Danielson Rice University 5/5/13 2 Images from Omniglot.com Why create a constructed script? Companion to a Conlang • Adds depth to the language/world • Can provide a social or historical feature • Provides the public face of a conlang • Can either enhance, or detract from, a conlang Companion to a Conlang • Tolkien’s constructed scripts • Tengwar • Cirth • Sarati • Many others… International Alphabets • International Phonetic AlphaBet (IPA) • Interbet • Universal Phonetic AlphaBet Shorthand Scripts Gregg Shorthand Shorthand Scripts Gregg Shorthand Other op<mizaon scripts Non-Linguistic Uses • Mathematical shorthand 0 < |x – x0| < δ ==> |f(x) – L| < ε • Musical notation • Computer Programming DO :1 <- #0¢#256 Educational Con-scripts • A novel way to introduce the study of languages in the classroom • Gets children excited aBout learning languages • Recruiting! The Neuroscience of Language Audiovisual Pathways • Connection Between how we process: • Written language • Speech • Emotion • Conlangers can play on this connection to create better scripts Synesthesia • Neurological disorder where phonemes/graphemes are associated with a sensory experience • Grapheme/color • Ordinal-Linguistic PersoniTication Synesthesia "T’s are generally crabbed, ungenerous creatures. U is a soulless sort of thing. 4 is honest, But… 3 I cannot trust… 9 is dark, a gentleman, tall and graceful, But politic under his suavity.” -Anonymous Synesthete I am a synesthete! (But so are all of you!) Kiki / Bouba Effect -
Proposal for Generation Panel for Sinhala Script Label
Proposal for the Generation Panel for the Sinhala Script Label Generation Ruleset for the Root Zone 1 General information Sinhala belongs to the Indo-European language family with its roots deeply associated with Indo-Aryan sub family to which the languages such as Persian and Hindi belong. Although it is not very clear whether people in Sri Lanka spoke a dialect of Prakrit at the time of arrival of Buddhism in the island, there is enough evidence that Sinhala evolved from mixing of Sanskrit, Magadi (the language which was spoken in Magada Province of India where Lord Buddha was born) and local language which was spoken by people of Sri Lanka prior to the arrival of Vijaya, the founder of Sinhala Kingdom. It is also surmised that Sinhala had evolved from an ancient variant of Apabramsa (middle Indic) which is known as ‘Elu’. When tracing history of Elu, it was preceded by Hela or Pali Sihala. Sinhala though has close relationships with Indo Aryan languages which are spoken primarily in the north, north eastern and central India, was very much influenced by Tamil which belongs to the Dravidian family of languages. Though Sinhala is related closely to Indic languages, it also has its own unique characteristics: Sinhala has symbols for two vowels which are not found in any other Indic languages in India: ‘æ’ (ඇ) and ‘æ:’ (ඈ). Sinhala script had evolved from Southern Brahmi script from which almost all the Southern Indic Scripts such as Telugu and Oriya had evolved. Later Sinhala was influenced by Pallava Grantha writing of Southern India. -
Con-Scripting the Masses: False Documents and Historical Revisionism in the Americas
University of Massachusetts Amherst ScholarWorks@UMass Amherst Open Access Dissertations 2-2011 Con-Scripting the Masses: False Documents and Historical Revisionism in the Americas Frans Weiser University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/open_access_dissertations Part of the Comparative Literature Commons Recommended Citation Weiser, Frans, "Con-Scripting the Masses: False Documents and Historical Revisionism in the Americas" (2011). Open Access Dissertations. 347. https://scholarworks.umass.edu/open_access_dissertations/347 This Open Access Dissertation is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Open Access Dissertations by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. CON-SCRIPTING THE MASSES: FALSE DOCUMENTS AND HISTORICAL REVISIONISM IN THE AMERICAS A Dissertation Presented by FRANS-STEPHEN WEISER Submitted to the Graduate School of the University of Massachusetts Amherst in partial fulfillment Of the requirements for the degree of DOCTOR OF PHILOSOPHY February 2011 Program of Comparative Literature © Copyright 2011 by Frans-Stephen Weiser All Rights Reserved CON-SCRIPTING THE MASSES: FALSE DOCUMENTS AND HISTORICAL REVISIONISM IN THE AMERICAS A Dissertation Presented by FRANS-STEPHEN WEISER Approved as to style and content by: _______________________________________________ David Lenson, Chair _______________________________________________ -
ED192569.Pdf
DOCUMENT RESUME ED 192 569 FL 011 690 AUTHOR MacDougall, Bonnie Graham: de Abrew, Kamini TITLE Sinhala: Basic Course. Module 1: Beginning Signsand Letters. IN Foreign Service (Dept. of State), Washington,D.C. Foreign Service Inst. PUB DATE 79 NOTE 119p.: For related documents, see FL 011 699-700. Photographs will not reproduce well. AVAILABLE FROM Superintendent of Documents, U.S. Government Printing Office, Washington, DC 20402 (No. 044-000-01764-4, $4.25) LANGUAGE English: Singhalese ErfS PRICE MF01/PC05 Plus Postage. DESCRIPTORS *Alphabets: Learning Modules: Postsecondary Education: Reading Instruction: SecondLanguage Instruction: *Singhalese: *Writing Instruction: Written Language ABSTRACT This course on the language of Sri Lanka is intended to be taken under d S=LL'hala-speaking instructor. Thismodule introduces the Sinhala writing system. The emphasisof the module is on letter recognition. Directions for writing the symbols in the "basic" alphabet are provided so that studentswill have a culturally appropriate and phonetically accurate method of writingdown words. Manv photographs of Sinhala signs are included. Each of2B lessons covers a specific aspect of reading and writing characters. With the addition of four practice reading sections at the conclusionof the module, this first part of the coursecan be completed in about 15 hours. ( 8) **** * ***** *** * * **** ******** * **** Reproductions supplied by ERRS are the best thatcan be made from the original document. *****4**************************************** **** ** MODULE I -
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. -
Finite-State Script Normalization and Processing Utilities: the Nisaba Brahmic Library
Finite-state script normalization and processing utilities: The Nisaba Brahmic library Cibu Johny† Lawrence Wolf-Sonkin‡ Alexander Gutkin† Brian Roark‡ Google Research †United Kingdom and ‡United States {cibu,wolfsonkin,agutkin,roark}@google.com Abstract In addition to such normalization issues, some scripts also have well-formedness constraints, i.e., This paper presents an open-source library for efficient low-level processing of ten ma- not all strings of Unicode characters from a single jor South Asian Brahmic scripts. The library script correspond to a valid (i.e., legible) grapheme provides a flexible and extensible framework sequence in the script. Such constraints do not ap- for supporting crucial operations on Brahmic ply in the basic Latin alphabet, where any permuta- scripts, such as NFC, visual normalization, tion of letters can be rendered as a valid string (e.g., reversible transliteration, and validity checks, for use as an acronym). The Brahmic family of implemented in Python within a finite-state scripts, however, including the Devanagari script transducer formalism. We survey some com- mon Brahmic script issues that may adversely used to write Hindi, Marathi and many other South affect the performance of downstream NLP Asian languages, do have such constraints. These tasks, and provide the rationale for finite-state scripts are alphasyllabaries, meaning that they are design and system implementation details. structured around orthographic syllables (aksara)̣ as the basic unit.1 One or more Unicode characters 1 Introduction combine when rendering one of thousands of leg- The Unicode Standard separates the representation ible aksara,̣ but many combinations do not corre- of text from its specific graphical rendering: text spond to any aksara.̣ Given a token in these scripts, is encoded as a sequence of characters, which, at one may want to (a) normalize it to a canonical presentation time are then collectively rendered form; and (b) check whether it is a well-formed into the appropriate sequence of glyphs for display. -
Sino-Tibetan Languages 393
Sino-Tibetan Languages 393 Gair J W (1998). Studies in South Asian linguistics: Sinhala Government Press. [Reprinted Sri Lanka Sahitya and other South Asian languages. Oxford: Oxford Uni- Mandalaya, Colombo: 1962.] versity Press. Karunatillake W S (1992). An introduction to spoken Sin- Gair J W & Karunatillake W S (1974). Literary Sinhala. hala. Colombo: Gunasena. Ithaca, NY: Cornell University South Asia Program. Karunatillake W S (2001). Historical phonology of Sinha- Gair J W & Karunatillake W S (1976). Literary Sinhala lese: from old Indo-Aryan to the 14th century AD. inflected forms: a synopsis with a transliteration guide to Colombo: S. Godage and Brothers. Sinhala script. Ithaca, NY: Cornell University South Asia Macdougall B G (1979). Sinhala: basic course. Program. Washington D.C.: Foreign Service Institute, Department Gair J W & Paolillo J C (1997). Sinhala (Languages of the of State. world/materials 34). Mu¨ nchen: Lincom. Matzel K & Jayawardena-Moser P (2001). Singhalesisch: Gair J W, Karunatillake W S & Paolillo J C (1987). Read- Eine Einfu¨ hrung. Wiesbaden: Harrassowitz. ings in colloquial Sinhala. Ithaca, NY: Cornell University Reynolds C H B (ed.) (1970). An anthology of Sinhalese South Asia Program. literature up to 1815. London: George Allen and Unwin Geiger W (1938). A grammar of the Sinhalese language. (English translations). Colombo: Royal Asiatic Society. Reynolds C H B (ed.) (1987). An anthology of Sinhalese Godakumbura C E (1955). Sinhalese literature. Colombo: literature of the twentieth century. Woodchurch, Kent: Colombo Apothecaries Ltd. Paul Norbury/Unesco (English translations). Gunasekara A M (1891). A grammar of the Sinhalese Reynolds C H B (1995). Sinhalese: an introductory course language. -
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset Brian Roarky, Lawrence Wolf-Sonkiny, Christo Kirovy, Sabrina J. Mielkez, Cibu Johnyy, Işın Demirşahiny and Keith Hally yGoogle Research zJohns Hopkins University {roark,wolfsonkin,ckirov,cibu,isin,kbhall}@google.com [email protected] Abstract This paper describes the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages. The dataset includes, for each language: 1) native script Wikipedia text; 2) a romanization lexicon; and 3) full sentence parallel data in both a native script of the language and the basic Latin alphabet. We document the methods used for preparation and selection of the Wikipedia text in each language; collection of attested romanizations for sampled lexicons; and manual romanization of held-out sentences from the native script collections. We additionally provide baseline results on several tasks made possible by the dataset, including single word transliteration, full sentence transliteration, and language modeling of native script and romanized text. Keywords: romanization, transliteration, South Asian languages 1. Introduction lingua franca, the prevalence of loanwords and code switch- Languages in South Asia – a region covering India, Pak- ing (largely but not exclusively with English) is high. istan, Bangladesh and neighboring countries – are generally Unlike translations, human generated parallel versions of written with Brahmic or Perso-Arabic scripts, but are also text in the native scripts of these languages and romanized often written in the Latin script, most notably for informal versions do not spontaneously occur in any meaningful vol- communication such as within SMS messages, WhatsApp, ume. -
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. -
1 Brahmi Word Recognition by Supervised Techniques
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 5 June 2020 doi:10.20944/preprints202006.0048.v1 Brahmi word recognition by supervised techniques Neha Gautam 1*, Soo See Chai1, Sadia Afrin2, Jais Jose3 1 Faculty of Computer Science and Information Technology, University Malaysia Sarawak 2 Institute of Cognitive Science, Universität Osnabrück 3Amity University, Noida, India *[email protected] [email protected] Abstract: Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words.