| dc.contributor.author | Prashant Katiyar, 19SCSE1180072 | |
| dc.contributor.author | Kumar Skand Kartik, 19SCSE1180062 | |
| dc.date.accessioned | 2024-09-18T06:11:12Z | |
| dc.date.available | 2024-09-18T06:11:12Z | |
| dc.date.issued | 2022-12 | |
| dc.identifier.uri | http://10.10.11.6/handle/1/18089 | |
| dc.description | SCHOOL OF COMPUTING SCIENCE AND ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
GALGOTIAS UNIVERSITY, GREATER NOIDA
INDIA | en_US |
| dc.description.abstract | People with speech disabilities communicate in sign language and therefore have trouble in
mingling with the able-bodied. There is a need for an interpretation system which could act as a
bridge between them and those who do not know their sign language. A functional unobtrusive
Indian sign language recognition system was implemented and tested on real world data. A
vocabulary of 26 symbols was collected. The vocabulary consisted mostly of two-handed signs
which were drawn from a wide repertoire of words of technical and daily-use origins.
Our project aims to create a computer application and train a model which when shown a real time
video of hand gestures of Indian Sign Language shows the output for that particular sign in text
format on the screen. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | Galgotias University | en_US |
| dc.subject | Human Action Recognition | en_US |
| dc.subject | Using Machine Learning | en_US |
| dc.title | Human Action Recognition Using Machine Learning | en_US |
| dc.type | Technical Report | en_US |
