Descriptor: Bengali Sign Word Recognition Video Dataset (BdSLW102)

Effective communication is the key component of human connection. However, individuals within the deaf and mute community face significant challenges due to the limited accessibility of their unique sign language. To address this issue, sign language emerges as an invaluable asset. Sign language recognition technology has the potential to bridge this communication gap. We have developed an extensive Bengali Word-Level Sign Video Dataset to address this crucial issue. This initiative has the potential to revolutionize our understanding of Bengali sign language. This dataset comprises two parts: words and sentences, each of which includes two sections, the raw video dataset and the masking dataset. The Masking Video Dataset has two types: with background and without background. For the word part, each section contains 102 classes with approximately 300 videos per class, resulting in approximately 91,800 videos overall. For the sentence part, each section comprises 20 classes with approximately 100 videos per class, resulting in approximately 6000 videos overall. The initial phase involved the selection of words and sentences to ensure the relevance of the dataset for real-life communication. A group of volunteers recorded hand gesture signs using smartphones and webcams. We processed the collected dataset using MediaPipe to convert the raw data into masked representations. Participant anonymity is ensured by using Haar Cascade detection, Gaussian blur, and AES encryption. Face data are securely encrypted and can be reconstructed by authorized users. This dataset is freely available for researchers, with dedicated models for recognition and identification. Our work holds the potential to transform communication for the deaf and mute community, advancing sign language recognition and improving lives.