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.
Aravind Ayyagari Reviewer
24 Sep 2024 05:24 PM
Approved
Relevance and Originality
The research tackles a critical issue in communication accessibility for the deaf and hard-of-hearing community. By focusing on automated sign language interpretation, it addresses a significant gap in current assistive technologies. The integration of artificial intelligence and image processing showcases originality in developing a system that translates Indian Sign Language into text and speech.
Methodology
The methodology involves using image sequences of double-handed signs and interpreting them through Python, which is a solid approach. However, details regarding the dataset, including size and diversity, would strengthen the methodology. Additionally, specifying the algorithms used for recognition and how they handle variability in signs would provide more clarity.
Validity & Reliability
The validity of the results hinges on the robustness of the recognition system. Using a well-defined dataset for training and testing is essential to ensure reliable outcomes. Addressing potential biases in the dataset and evaluation metrics would enhance the credibility of the findings.
Clarity and Structure
The article is generally clear, but some technical terms may need further explanation for broader comprehension. A more structured layout with defined sections could improve readability. Breaking down complex ideas into simpler components would help convey the information more effectively.
Result Analysis
While the project aims to convert signs into text and speech, specific performance metrics are necessary to evaluate the effectiveness of the system. Including metrics like accuracy, precision, and recall would provide a clearer understanding of its performance. Visual examples of successful translations could further illustrate the system's capabilities.
IJ Publication Publisher
Thank You Sir
Aravind Ayyagari Reviewer