The Role of Computers in the Development of Odia Language

Any language computer can be useful if the above two requirements are met. But we can only accept such behavior as a modern writing system. In other words, in the past we used to write with palm leaves and pencils, then we used to write on paper, then we used typewriters and typewriters. Now we can write via computer. This is the best way to write and print. This requirement is available in almost all languages, this requirement has been met by the Odia language since the 180s, which means that this year we have been able to type the letters of the Odia language on the computer and print it on the printer. This is the first step in using a computer language. The second step in making a language useful to a computer is: understanding the computer. Computers understand a language through a variety of programs. This requires connecting the operating system to a new language. It is imperative that the language be integrated into the Unicode system. Unicode has provided a code for the scripts of the world's most computer-friendly languages. This code can be understood by the operating system. As a result, it is possible to Unicode the computer by naming that language, sorting it from scratch, searching for a file or folder named in that language, deleting any incorrect word koji, and so on. Fortunately, the Odia language scripts were included in the Unicode in 2006. Odia is the third largest Indian language in this regard. Keywords: typewriters, computer, languages, Unicode, folder, deleting, scripts, Odia, Indian language,

Optical Character Recognition of Indian Language Manuscripts using Convolutional Neural Networks

India's constitution has 22 languages written in 17 different scripts. These materials have a limited lifespan, and as generations pass, these materials deteriorate, and the vital knowledge is lost. This work uses digital texts to convey information to future generations. Optical Character Recognition (OCR) helps extract information from scanned manuscripts (printed text). This paper proposes a simple and effective solution of optical character recognition (OCR) Sanskrit Character from text document images using long short-term memory (LSTM) and neural networks of Sanskrit Characters. Existing methods focuses only upon the single touching characters. But our main focus is to design a robust method using Bidirectional Long Short-Term Memory (BLSTM) architecture for overlapping lines, touching characters in middle and upper zone and half character which would increase the accuracy of the present OCR system for recognition of poorly maintained Sanskrit literature.

Development and validation of a novel corpus of 269 verb pictures in Kannada based on an argument structure taxonomy

Pictorial stimuli are crucial in psycholinguistic research and clinical practice. The development of culturally and linguistically appropriate, standardized picture corpora is a tedious and meticulous process. Yet, such readily accessible picture sets are useful for researchers and clinicians alike. The current study introduces a novel set of 269 verb pictures for an Indian language – Kannada. The included verbs were selected from a published database of 100,000 words along with their frequency scores in this language, and were subsequently categorized based on an argument structure taxonomy. Each picture is developed based on an exemplar sentence that depicts a scenario rather than a mere action. Norms are provided for verb name and argument agreement, image agreement, concept familiarity, and visual complexity, along with the orthographic frequency. Correlations between these measures are also described. The complete set of pictures are freely downloadable from https://osf.io/uk2af/?view_only=ecffbd92f48546a484c869b3f0b8ec94 for academic, research, and clinical usage in the future.

Recognizing Ancient South Indian Language Using Artificial Neural Network Associate With Opposition Based Grey Wolf Optimization Algorithm

Recognizing signs and fonts of prehistoric language is a fairly difficult job that require special tools. This stipulation makes the dispensation period overriding, difficult, and tiresome to calculate. This paper presents a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA). It identifies the prehistoric language, signs and fonts. It is apparent from the ANN system that arbitrarily produced weights or neurons linking various layers plays a significant role in its performance. For adaptively determining these weights, this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization, Particle Swarm Optimization and Grey Wolf Optimization to the ANN system. Performance results have illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques. In test case 1, the accuracy value of OGWO is 94.89% and in test case 2, the accuracy value of OGWO is 92.34%, on average, the accuracy of OGWO achieves 5.8% greater accuracy than ANN-GWO, 10.1% greater accuracy than ANN-PSO and 22.1% greater accuracy over conventional ANN technique.

Indian English is also Creole: Incorporating Regional Bias in Research Pedagogy

Research pedagogy in India should readjust itself to accommodate claims of regional autonomy in arts and letters. Different ways of reconstructing a pedagogy of research are recommended. Reflexive Humanism ensures adequate assessment and evaluation of cultural, literary, and aesthetic achievements of diverse populations. The Indian English corpus is redefined as a creolized Indian language with lexical and semantic factors borrowed from English. The consciousness of pro-national subjectivism is also considered an essential constituent of Indian English literature. Lines of research are suggested for aspiring scholars in the Indian academy. The author emphasizes a dynamic and sensitive adaptation of research methodology which respects and reintegrates itself with the evolution of globally aware, contemporary society in India.

Cross-language comparison of long-term average speech spectrum and dynamic range for three Indian languages and British English

Purpose: The Long-Term Average Speech Spectrum (LTASS) and Dynamic Range (DR) of speech strongly influence estimates of Speech Intelligibility Index (SII), gain and compression required for hearing aid fitting. It is also known that acoustic and linguistic characteristics of a language have a bearing on its LTASS and DR. Thus, there is a need to estimate LTASS and DR for Indian languages. The present work on three Indian languages fills this gap and contrasts LTASS and DR attributes of these languages against British English.Methods: For this purpose, LTASS and DR were measured for 21 one-third octave bands in the frequency range of 0.1 to 10 kHz for Hindi, Kannada, Indian English and British English.Results: Our work shows that the DR of Indian languages studied is 7-10 dB less relative to that of British English. We also report that LTASS levels for Indian languages are 7 dB lower relative to British English for frequencies above 1 kHz. Finally, we observed that LTASS and DR attributes across genders were more or less the same.Conclusions: Given the evidence presented in this work that LTASS and DR characteristics for Indian languages analyzed are markedly different than those for BE, there is a need to determine Indian language specific SII, as well as gain and compression parameters used in hearing aids.