Adobe Devanagari typeface

Typeface design: collaborative work commissioned by Adobe Inc. Published but unreleased. The Adobe Devanagari typefaces were commissioned from Tiro Typeworks and collaboratively designed by Tim Holloway, Fiona Ross and John Hudson, beginning in 2005. The types were officially released in 2009. The design brief was to produce a typeface for modern business communications in Hindi and other languages, to be legible both in print and on screen. Adobe Devanagari was designed to be highly readable in a range of situations including quite small sizes in spreadsheets and in continuous text setting, as well as at display sizes, where the full character of the typeface reveals itself. The construction of the letters is based on traditional penmanship but possesses less stroke contrast than many Devanagari types, in order to maintain strong, legible forms at smaller sizes. To achieve a dynamic, fluid style the design features a rounded treatment of distinguishing terminals and stroke reversals, open counters that also aid legibility at smaller sizes, and delicately flaring strokes. Together, these details reveal an original hand and provide a contemporary approach that is clean, clear and comfortable to read whether in short or long passages of text. This new approach to a traditional script is intended to counter the dominance of rigid, staccato-like effects of straight verticals and horizontals in earlier types and many existing fonts. OpenType Layout features in the fonts provide both automated and discretionary access to an extensive glyph set, enabling sophisticated typography. Many conjuncts preferred in classical literary texts and particularly in some North Indian languages are included; these literary conjuncts may be substituted by specially designed alternative linear forms and fitted half forms. The length of the ikars—ि and ी—varies automatically according to adjacent letter or conjunct width. Regional variants of characters and numerals (e.g. Marathi forms) are included as alternates. Careful attention has been given to the placements of all vowel signs and modifiers. The fonts include both proportional and tabular numerals in Indian and European styles. Extensive kerning covers several thousand possible combinations of half forms and full forms to anticipate arbitrary conjuncts in foreign loan words. _____

Laghu-siddhānta-kaumudī /

Vol. 3, 6 issued without series statement.

Linguistic survey of India.

"Except in a few trifling instances, each dialect and sub-dialect is represented by a version of the parable of the prodigal son, printed in the vernacular character when such exists, and also in the roman character ... Other specimens of the more important dialects are also given. These are mainly pieces of folklore recorded in the actual words of the persons who narrated them"--Note inserted in vol. V, pt. 1.

LOKASAHITHYA KE VISESH SANDARBH MEIN HINDI-MALAYALAM BALASAHITHYA KA VISHLESHANATMAK ADHYAYAN

Hindi

Social gender stereotypes and their implications in Hindi

This essay looks at the subtle ways in which gender identities are constructed and reinforced in India through social norms of language use. Language itself becomes a medium for perpetuating gender stereotypes, forcing its speakers to confirm to socially defined gender roles. Using examples from a classroom discussion about a film, this essay will highlight the underlying rigid male-female stereotypes in Indian society with their more obvious expressions in language. For the urban woman in India globalisation meant increased economic equality and exposure to changed lifestyles. On an individual level it also meant redefining gender relations and changing the hierarchy in man-woman relationships. With the economic independence there is a heightened sense of liberation in all spheres of social life, a confidence to fuzz the rigid boundaries of gender roles. With the new films and media celebrating this liberated woman, who is ready to assert her sexual needs, who is ready to explode those long held notions of morality, one would expect that the changes are not just superficial. But as it soon became obvious in the course of a classroom discussion about relationships and stereotypes related to age, the surface changes can not become part of the common vocabulary, for the obvious reason that there is still a vast gap between the screen image of this new woman and the ground reality. Social considerations define the limits of this assertiveness of women, whereas men are happy to be liberal within the larger frame of social sanctions. The educated urban woman in India speaks in favour of change and the educated urban male supports her, but one just needs to scratch the surface to see the time tested formulae of gender roles firmly in place. The way the urban woman happily balances this emerging promise of independence with her gendered social identity, makes it necessary to rethink some aspects of looking at gender in a gradually changing, traditional society like India.

Design And Development Of A Named Entity Based Question Answering System For Malayalam Language

This is a Named Entity Based Question Answering System for Malayalam Language. Although a vast amount of information is available today in digital form, no effective information access mechanism exists to provide humans with convenient information access. Information Retrieval and Question Answering systems are the two mechanisms available now for information access. Information systems typically return a long list of documents in response to a user’s query which are to be skimmed by the user to determine whether they contain an answer. But a Question Answering System allows the user to state his/her information need as a natural language question and receives most appropriate answer in a word or a sentence or a paragraph. This system is based on Named Entity Tagging and Question Classification. Document tagging extracts useful information from the documents which will be used in finding the answer to the question. Question Classification extracts useful information from the question to determine the type of the question and the way in which the question is to be answered. Various Machine Learning methods are used to tag the documents. Rule-Based Approach is used for Question Classification. Malayalam belongs to the Dravidian family of languages and is one of the four major languages of this family. It is one of the 22 Scheduled Languages of India with official language status in the state of Kerala. It is spoken by 40 million people. Malayalam is a morphologically rich agglutinative language and relatively of free word order. Also Malayalam has a productive morphology that allows the creation of complex words which are often highly ambiguous. Document tagging tools such as Parts-of-Speech Tagger, Phrase Chunker, Named Entity Tagger, and Compound Word Splitter are developed as a part of this research work. No such tools were available for Malayalam language. Finite State Transducer, High Order Conditional Random Field, Artificial Immunity System Principles, and Support Vector Machines are the techniques used for the design of these document preprocessing tools. This research work describes how the Named Entity is used to represent the documents. Single sentence questions are used to test the system. Overall Precision and Recall obtained are 88.5% and 85.9% respectively. This work can be extended in several directions. The coverage of non-factoid questions can be increased and also it can be extended to include open domain applications. Reference Resolution and Word Sense Disambiguation techniques are suggested as the future enhancements

Studies on a syntax based approach for translation between structurally different languages and the development of a prototype for Malayalam to English translation

This thesis summarizes the results on the studies on a syntax based approach for translation between Malayalam, one of Dravidian languages and English and also on the development of the major modules in building a prototype machine translation system from Malayalam to English. The development of the system is a pioneering effort in Malayalam language unattempted by previous researchers. The computational models chosen for the system is first of its kind for Malayalam language. An in depth study has been carried out in the design of the computational models and data structures needed for different modules: morphological analyzer , a parser, a syntactic structure transfer module and target language sentence generator required for the prototype system. The generation of list of part of speech tags, chunk tags and the hierarchical dependencies among the chunks required for the translation process also has been done. In the development process, the major goals are: (a) accuracy of translation (b) speed and (c) space. Accuracy-wise, smart tools for handling transfer grammar and translation standards including equivalent words, expressions, phrases and styles in the target language are to be developed. The grammar should be optimized with a view to obtaining a single correct parse and hence a single translated output. Speed-wise, innovative use of corpus analysis, efficient parsing algorithm, design of efficient Data Structure and run-time frequency-based rearrangement of the grammar which substantially reduces the parsing and generation time are required. The space requirement also has to be minimised

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

Alignment Model and Training Technique in SMT from English to Malayalam

This paper investigates certain methods of training adopted in the Statistical Machine Translator (SMT) from English to Malayalam. In English Malayalam SMT, the word to word translation is determined by training the parallel corpus. Our primary goal is to improve the alignment model by reducing the number of possible alignments of all sentence pairs present in the bilingual corpus. Incorporating morphological information into the parallel corpus with the help of the parts of speech tagger has brought around better training results with improved accuracy

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Malayalam Isolated Digit Recognition using HMM and PLP cepstral coefficient

Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data