Enhancing Accessibility to Government Services in India through Natural Language Processing (NLP): Bridging Language Barriers and Supporting Literacy

Natural Language Processing (NLP), a subset of artificial intelligence, has advanced from rule-based models to deep learning algorithms that can understand and respond to natural human language. By leveraging this technology, government portals can automate chatbots, and make services more accessible to those who face language barriers and literacy challenges. This paper examines the challenges faced by internal migrants and illiterate individuals in India while attempting to access essential services such as healthcare, education and financial aid. Two NLP-based solutions are presented: an on-site terminal to assist users in government offices and a remote, web-based service accessible through personal devices such as phones and laptops. Both of the solutions listed aim to translate spoken language and simplify the submission of documents. Despite some initial costs and maintenance, the long-term benefits of enhanced accessibility and reduced operational burdens justify these costs. Ultimately, adopting NLP in government services can contribute to a more inclusive and equitable society, and improve the quality of life for millions.

Authors

Publication Details

Journal
International Journal of Science and Research (IJSR)
Published
2024-09-05
DOI
https://doi.org/10.21275/sr24911203340
Primary Topic
Text Readability and Simplification
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Accessibility to Government Services in India through Natural Language Processing (NLP): Bridging Language Barriers and Supporting Literacy

Abhijay Parija
International Journal of Science and Research (IJSR)
Text Readability and Simplification
article

Enhancing Accessibility to Government Services in India through Natural Language Processing (NLP): Bridging Language Barriers and Supporting Literacy

Abhijay Parija
article en

Abstract

Natural Language Processing (NLP), a subset of artificial intelligence, has advanced from rule-based models to deep learning algorithms that can understand and respond to natural human language. By leveraging this technology, government portals can automate chatbots, and make services more accessible to those who face language barriers and literacy challenges. This paper examines the challenges faced by internal migrants and illiterate individuals in India while attempting to access essential services such as healthcare, education and financial aid. Two NLP-based solutions are presented: an on-site terminal to assist users in government offices and a remote, web-based service accessible through personal devices such as phones and laptops. Both of the solutions listed aim to translate spoken language and simplify the submission of documents. Despite some initial costs and maintenance, the long-term benefits of enhanced accessibility and reduced operational burdens justify these costs. Ultimately, adopting NLP in government services can contribute to a more inclusive and equitable society, and improve the quality of life for millions.

International Journal of Science and Research (IJSR)Vol. 13(9)
Quality Education
Openalex Percentile: Top 86%
Text Readability and Simplification
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Enhancing Accessibility to Government Services in India through Natural Language Processing (NLP): Bridging Language Barriers and Supporting Literacy — Abhijay Parija · International Journal of Science and Research (IJSR) (2024) | TGRS Research Map | TGRS