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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. -
The Festvox Indic Frontend for Grapheme-To-Phoneme Conversion
The Festvox Indic Frontend for Grapheme-to-Phoneme Conversion Alok Parlikar, Sunayana Sitaram, Andrew Wilkinson and Alan W Black Carnegie Mellon University Pittsburgh, USA aup, ssitaram, aewilkin, [email protected] Abstract Text-to-Speech (TTS) systems convert text into phonetic pronunciations which are then processed by Acoustic Models. TTS frontends typically include text processing, lexical lookup and Grapheme-to-Phoneme (g2p) conversion stages. This paper describes the design and implementation of the Indic frontend, which provides explicit support for many major Indian languages, along with a unified framework with easy extensibility for other Indian languages. The Indic frontend handles many phenomena common to Indian languages such as schwa deletion, contextual nasalization, and voicing. It also handles multi-script synthesis between various Indian-language scripts and English. We describe experiments comparing the quality of TTS systems built using the Indic frontend to grapheme-based systems. While this frontend was designed keeping TTS in mind, it can also be used as a general g2p system for Automatic Speech Recognition. Keywords: speech synthesis, Indian language resources, pronunciation 1. Introduction in models of the spectrum and the prosody. Another prob- lem with this approach is that since each grapheme maps Intelligible and natural-sounding Text-to-Speech to a single “phoneme” in all contexts, this technique does (TTS) systems exist for a number of languages of the world not work well in the case of languages that have pronun- today. However, for low-resource, high-population lan- ciation ambiguities. We refer to this technique as “Raw guages, such as languages of the Indian subcontinent, there Graphemes.” are very few high-quality TTS systems available. -
Myanmar Script Learning Guide Burmese 1,2,3 Tone System Is Developed by Naing Tin-Nyunt-Pu Copyright © 2013-2017, Naing Tinnyuntpu & Asia Pearl Travels
Myanmar Script Learning guide (Revision F) by Naing Tinnyuntpu WITH FREE ONLINE AUDIO SUPPORT https://www.asiapearltravels.com/language/myanmar-script-learning-guide.php 1 of 104 Menu ≡ Introduction ........................................................................................4 Vowel: A .............................................................................................6 Vowel: E or I ....................................................................................13 Vowel: U ..........................................................................................20 Vowel: O ..........................................................................................24 Vowel: Ay .........................................................................................28 Vowel: Au ........................................................................................33 Vowel: Un or An ..............................................................................37 Vowel: In ..........................................................................................42 Vowel: Eare ......................................................................................46 Vowel: Ain .......................................................................................51 Vowel: Ome .....................................................................................53 Vowel: Ine ........................................................................................56 Vowel: Oon ......................................................................................59 -
An Introduction to Old Persian Prods Oktor Skjærvø
An Introduction to Old Persian Prods Oktor Skjærvø Copyright © 2016 by Prods Oktor Skjærvø Please do not cite in print without the author’s permission. This Introduction may be distributed freely as a service to teachers and students of Old Iranian. In my experience, it can be taught as a one-term full course at 4 hrs/w. My thanks to all of my students and colleagues, who have actively noted typos, inconsistencies of presentation, etc. TABLE OF CONTENTS Select bibliography ................................................................................................................................... 9 Sigla and Abbreviations ........................................................................................................................... 12 Lesson 1 ..................................................................................................................................................... 13 Old Persian and old Iranian. .................................................................................................................... 13 Script. Origin. .......................................................................................................................................... 14 Script. Writing system. ........................................................................................................................... 14 The syllabary. .......................................................................................................................................... 15 Logograms. ............................................................................................................................................ -
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. -
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. -
An Introduction to Indic Scripts
An Introduction to Indic Scripts Richard Ishida W3C [email protected] HTML version: http://www.w3.org/2002/Talks/09-ri-indic/indic-paper.html PDF version: http://www.w3.org/2002/Talks/09-ri-indic/indic-paper.pdf Introduction This paper provides an introduction to the major Indic scripts used on the Indian mainland. Those addressed in this paper include specifically Bengali, Devanagari, Gujarati, Gurmukhi, Kannada, Malayalam, Oriya, Tamil, and Telugu. I have used XHTML encoded in UTF-8 for the base version of this paper. Most of the XHTML file can be viewed if you are running Windows XP with all associated Indic font and rendering support, and the Arial Unicode MS font. For examples that require complex rendering in scripts not yet supported by this configuration, such as Bengali, Oriya, and Malayalam, I have used non- Unicode fonts supplied with Gamma's Unitype. To view all fonts as intended without the above you can view the PDF file whose URL is given above. Although the Indic scripts are often described as similar, there is a large amount of variation at the detailed implementation level. To provide a detailed account of how each Indic script implements particular features on a letter by letter basis would require too much time and space for the task at hand. Nevertheless, despite the detail variations, the basic mechanisms are to a large extent the same, and at the general level there is a great deal of similarity between these scripts. It is certainly possible to structure a discussion of the relevant features along the same lines for each of the scripts in the set. -
Designing Soft Keyboards for Brahmic Scripts
Designing Soft Keyboards for Brahmic Scripts Lauren Hinkle Miguel Lezcano Jugal Kalita Colorado College University of Colorado University of Colorado Colorado Springs, CO, USA Colorado Springs, CO, USA Colorado Springs, CO, USA lauren.hinkle [email protected] [email protected] @coloradocollege.edu Abstract Soft keyboards allow a user to input text with and without the use of a physical keyboard. They Despite being spoken by a large percent- are versatile because they allow data to be input age of the world, many Indic languages through mouse clicks on an on-screen keyboard, have escaped research and development in through a touch screen on a computer, cell phone, the realm of text-input. There exist lan- or PDA, or by mapping a virtual keyboard to guages with poor or no support for typ- a standard physical keyboard. With the recent ing. Soft keyboards, because of their ease surge in popularity of touchscreen media such of installation and lack of reliance on spe- as cell phones and computers, well-designed cific hardware, are a promising solution as soft keyboards are becoming more important an input device for many languages. De- everywhere. veloping an acceptable soft keyboard re- For languages that don’t conform well to quires frequency analysis of characters in standard QWERTY based keyboards that order to design a layout that minimizes accompany most computers, soft keyboards are text-input time. This paper proposes us- especially important. Brahmic scripts (Coulmas ing various development techniques, lay- 1990) have more characters and ligatures than fit out variations, and evaluation methods usably on a standard keyboard. -
Second Language Writing System Word Recognition (With a Focus on Lao)
Second Language Writing System Word Recognition (with a focus on Lao) Christine Elliott University of Wisconsin-Madison Abstract Learning a second language (L2) with a script different from the learner’s first language (L1) presents unique challenges for both stu- dent and teacher. This paper looks at current theory and research examining issues of second language writing system (L2WS) acquisi- tion, particularly issues pertaining to decoding and word recognition1 by adult learners. I argue that the importance of word recognition and decoding in fluent L1 and L2 reading has been overshadowed for several decades by a focus on research looking at top-down reading processes. Although top-down reading processes and strategies are clearly components of successful L2 reading, I argue that more atten- tion needs to be given to bottom-up processing skills, particularly for beginning learners of an L2 that uses a script that is different from their L1. I use the example of learning Lao as a second language writing system where possible and suggest preliminary pedagogical implications. Introduction Second language writing systems have increasingly become the focus of a growing body of research drawing on the fields of psy- chology, education, linguistics, and second language acquisition, among others. The term writing system is used to refer to the ways in which written symbols represent language in a systematic way (Cook and Bassetti, 2005). Further, a writing system can be discussed in terms of both its script and its orthography. Cook and Bassetti de- fine script as the physical implementation of a writing system (i.e. the written symbols) and orthography as “the rules for using a script in a 1 Following Koda (2005), I define word recognition as “the process of extract- ing lexical information from graphic displays of words,” and decoding as the specific process of extracting phonological information. -
Simplified Abugidas
Simplified Abugidas Chenchen Ding, Masao Utiyama, and Eiichiro Sumita Advanced Translation Technology Laboratory, Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0289, Japan fchenchen.ding, mutiyama, [email protected] phonogram segmental abjad Abstract writing alphabet system An abugida is a writing system where the logogram syllabic abugida consonant letters represent syllables with Figure 1: Hierarchy of writing systems. a default vowel and other vowels are de- noted by diacritics. We investigate the fea- ណូ ន ណណន នួន ននន …ជិតណណន… sibility of recovering the original text writ- /noon/ /naen/ /nuən/ /nein/ vowel machine ten in an abugida after omitting subordi- diacritic omission learning ណន នន methods nate diacritics and merging consonant let- consonant character ters with similar phonetic values. This is merging N N … J T N N … crucial for developing more efficient in- (a) ABUGIDA SIMPLIFICATION (b) RECOVERY put methods by reducing the complexity in abugidas. Four abugidas in the south- Figure 2: Overview of the approach in this study. ern Brahmic family, i.e., Thai, Burmese, ters equally. In contrast, abjads (e.g., the Arabic Khmer, and Lao, were studied using a and Hebrew scripts) do not write most vowels ex- newswire 20; 000-sentence dataset. We plicitly. The third type, abugidas, also called al- compared the recovery performance of a phasyllabary, includes features from both segmen- support vector machine and an LSTM- tal and syllabic systems. In abugidas, consonant based recurrent neural network, finding letters represent syllables with a default vowel, that the abugida graphemes could be re- and other vowels are denoted by diacritics. -
14 South and Central Asia-III 14 Ancient Scripts
The Unicode® Standard Version 12.0 – 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. © 2019 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 12.0. Includes index. ISBN 978-1-936213-22-1 (http://www.unicode.org/versions/Unicode12.0.0/) 1.