Pakistan sign language recognition: leveraging deep learning models with limited dataset

Humans communicate with each other through natural language, such as speech and written words. However for the deaf people, the sign language is the only means of conveying information. Without an interpreter, they are unable to converged with each other. Because of this, the implementation of a technology that understands sign language would be of significant help to the social lives of deaf people. Indian sign language (ISL) uses both hands to make gestures instead of one hand unlike ASL. The suggested approach is to develop an ISL recognition system which converts the sign language into readable text. This can be done by various approaches like smart gloves, object detection and pose detection. The proposed method uses a tensorflow object detection for detecting hand moments. Mobile-net is used yo extract feature with the help of and further detection by using single shot multi-box detection (SSD). The system can able to detect 26 of 26 ISL signs in real time with mAP score 96.1 %

Real-Time Indian Sign Language Detection using SSD-Mobilenet

The use of sign language is essential for deaf and hard-of-hearing individuals to communicate with the world. Traditional methods involving sensors and image processing techniques like edge detection and Hough Transform are often expensive and complex. This research tackles real-time fingerspelling recognition in sign language through advanced machine-learning techniques. A dataset of five distinct gestures was created using webcam images, which the system processes to display corresponding characters on a screen in real time. Leveraging a pre-trained SSD MobileNet V2 model for transfer learning, the system achieves robust and consistent classification of sign language gestures, with an accuracy of over $80 \%$ under various lighting conditions and backgrounds. This approach bridges the communication gap for deaf individuals, enabling effective interaction through hand gestures. The system is user-friendly and scalable, suitable for educational tools, assistive technologies, and real-time communication aids. Future work will expand the gesture dataset, enhance accuracy, and improve robustness in diverse environments, including integrating more complex sign language gestures and phrases to further enhance the system’s utility and performance.

Sign Language Translation using Singleshot Mobilenet V2

India is a Multilanguage country and Kannada is one of Indian historical language spoken by Millions of people. Kannada has a different versions from region to region. There exist different efficient algorithms to convert the formal language using a standard dictionary. Whereas converting the informal Kannada language to another language is a Challenging task, since each word of the language as a range of versions. The chief objective of the paper is to translate the informal Kannada language by understating the native vocabulary. This paper discusses about assessment approach to convert dialects of Kannada into English. For many native languages, particularly those with dialects, translation has been an unscathed field. When the source material contains dialects other than the official Kannada, existing translators fail to produce an accurate translation. This paper proposes LSTM algorithm to develop the translator. Automatic Speech Recognition (ASR) and other Translation models are used for accurate translation. The test pattern in this paper represents an improvement in the translation, with the validation accuracy of $86.32 \%$, thereby decreasing word Error Rate (WER). 

Translation of Different Dialects of Kannada to English Using Machine Learning

Communication is crucial in our day-to-day lives. It is the basis for all human interactions. It is how people pass on information to other people and receive information from them. A person with a speaking or hearing problem cannot communicate properly with others. A person who isdeaf or dumb usually uses Sign Language to communicate with others. Normal individuals who would not know Sign language, on the other hand, cannot communicate better with them. Indian Sign Language acts as a mode of communication for deaf and dumb people who constitute a hefty portion of the Indian population. Most people struggle to apprehend ISL gestures. As a result, communication gap is created between the hearing and speech impaired and those who do not understand ISL. Translation systems are required to help bridge the gap between the community of people withhearing and speechimpairment and the general population. This paper focuses on developing an end-to-end system that can recognize the spoken language and interpret the corresponding speech to animated sign language gestures and that is also capable of converting Indian Sign Language to speech.

Indian Sign Language Translator

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Indian Sign Language Translator