Device for Spoken Word and Captioning of Illustrations

This project shows the creation of a unique app that can take a picture as input, make a descriptive description for it, and then turn that caption into speech. The system uses cutting-edge models for both picture captioning and text-to-speech synthesis. This makes sure that the process from capturing an image to producing sound is smooth and quick. To do this, the app uses Hugging Face Transformers for model inference, which lets you use models that have already been trained and makes it easier to add advanced deep learning methods. The web interface was made with Streamlit, which makes it easy for people to connect with each other. A number of tools were also used to handle the processing of both audio and video data. This made sure that the application worked well at all times. In terms of accessible technology, this app is a big step forward. It gives people who are blind or have low vision a useful tool that can help them interact with visual material in a more meaningful way. This app is a great way to help bridge the gap between visual and auditory material because it uses cutting-edge technology and is designed to be accessible to everyone.

Authors

Publication Details

Journal
International Journal of Computer Science and Mobile Computing
Published
2024-09-21
DOI
https://doi.org/10.47760/ijcsmc.2024.v13i09.002
Citations
1
Primary Topic
Subtitles and Audiovisual Media
Type
article
Field-Weighted Citation Impact
2.78
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Device for Spoken Word and Captioning of Illustrations

Sunil Kumar, Boriya Siddhant
1 citations
International Journal of Computer Science and Mobile Computing
Subtitles and Audiovisual Media
2.78
article

Device for Spoken Word and Captioning of Illustrations

Sunil Kumar, Boriya Siddhant
article en
1 citations

Abstract

This project shows the creation of a unique app that can take a picture as input, make a descriptive description for it, and then turn that caption into speech. The system uses cutting-edge models for both picture captioning and text-to-speech synthesis. This makes sure that the process from capturing an image to producing sound is smooth and quick. To do this, the app uses Hugging Face Transformers for model inference, which lets you use models that have already been trained and makes it easier to add advanced deep learning methods. The web interface was made with Streamlit, which makes it easy for people to connect with each other. A number of tools were also used to handle the processing of both audio and video data. This made sure that the application worked well at all times. In terms of accessible technology, this app is a big step forward. It gives people who are blind or have low vision a useful tool that can help them interact with visual material in a more meaningful way. This app is a great way to help bridge the gap between visual and auditory material because it uses cutting-edge technology and is designed to be accessible to everyone.

International Journal of Computer Science and Mobile ComputingVol. 13(9)
Openalex Percentile: Top 9%
Subtitles and Audiovisual Media
2.78
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.

Device for Spoken Word and Captioning of Illustrations — Sunil Kumar, Boriya Siddhant · International Journal of Computer Science and Mobile Computing (2024) | TGRS Research Map | TGRS