Transparent Peer Review By Scholar9
Sign language Detection and Recognization using Machine Learning
Abstract
Humans usually communicate with their voices and languages. We understand one another's ideas because we can listen. Even now, speech recognition allows us to issue orders. What happens if someone is totally deaf and mute? As a result, the vast majority of people who are hard of hearing or deaf communicate via sign language. A lot of effort is being put into the field of automated sign language interpretation in order to guarantee that these people may carry on living their lives without any problems. This topic has seen the development of numerous approaches and algorithms that combine artificial intelligence and image processing techniques. All sign language recognition systems have been taught to accurately recognize and interpret signs. This project takes sequences of images depicting double-handed Indian Sign Language, interprets them with Python, and outputs both text and speech as part of a larger system to bring speech to the wordless.
Chandrasekhara (Samba) Mokkapati Reviewer
24 Sep 2024 05:41 PM
Approved
Relevance and Originality
This project addresses a crucial need for effective communication tools for the deaf and hard-of-hearing community, focusing specifically on Indian Sign Language. By leveraging artificial intelligence and image processing, it presents an original approach to automated sign language interpretation, making a significant contribution to both technology and social inclusion.
Methodology
The methodology should detail the specific algorithms and techniques employed for interpreting sequences of images in Indian Sign Language. It would be beneficial to describe the data collection process, including the size and diversity of the dataset used, as well as the Python libraries and frameworks applied in the development of the system. Clear steps in the algorithmic process will enhance understanding of the implementation.
Validity & Reliability
To ensure validity, the project should outline how results were validated, perhaps through comparisons with existing sign language recognition systems or expert evaluations. Information on the reliability of the data collection methods and the consistency of performance across different scenarios will strengthen the credibility of the findings. Addressing any limitations in the dataset or recognition capabilities would also be important.
Clarity and Structure
The project should be organized into well-defined sections, including an introduction, methodology, results, and discussion, allowing for easy navigation. Each section should logically flow into the next, with clear explanations of technical terms to ensure accessibility for a broader audience. Visual aids, such as flowcharts or examples of interpreted signs, could enhance clarity.
Result Analysis
The result analysis should not only present the accuracy and performance metrics of the sign language recognition system but also discuss the implications of these results for users and stakeholders in the deaf community. A comparative analysis with traditional methods of sign language communication could illustrate the advantages of the proposed system. Additionally, a discussion of potential areas for future development and improvement will provide valuable insights for ongoing research in this field.
IJ Publication Publisher
Ok Sir
Chandrasekhara (Samba) Mokkapati Reviewer