Sign language discourse is an essential mode of daily communication for the
deaf and hard-of-hearing people. However, research on Bangla Sign Language
(BdSL) faces notable limitations, primarily due to the lack of datasets.
Recognizing wordlevel signs in BdSL (WL-BdSL) presents a multitude of
challenges, including the need for well-annotated datasets, capturing the
dynamic nature of sign gestures from facial or hand landmarks, developing
suitable machine learning or deep learning-based models with substantial video
samples, and so on. In this paper, we address these challenges by creating a
comprehensive BdSL word-level dataset named BdSLW60 in an unconstrained and
natural setting, allowing positional and temporal variations and allowing sign
users to change hand dominance freely. The dataset encompasses 60 Bangla sign
words, with a significant scale of 9307 video trials provided by 18 signers
under the supervision of a sign language professional. The dataset was
rigorously annotated and cross-checked by 60 annotators. We also introduced a
unique approach of a relative quantization-based key frame encoding technique
for landmark based sign gesture recognition. We report the benchmarking of our
BdSLW60 dataset using the Support Vector Machine (SVM) with testing accuracy up
to 67.6% and an attention-based bi-LSTM with testing accuracy up to 75.1%. The
dataset is available at this https URL and
the code base is accessible from this https URL

本文介绍了一种新的方法，基于一个全面的 Bangla 手势词级数据集（BdSLW60），通过在不受约束的自然环境中捕捉手势的动态特性，允许位置和时间上的变化，并允许手势使用者自由更改手势的手的主导性，进而实现手势识别任务。