In the domain of Bangla Sign Language (BdSL) interpretation, prior approaches
often imposed a burden on users, requiring them to spell words without hidden
characters, which were subsequently corrected using Bangla grammar rules due to
the missing classes in BdSL36 dataset. However, this method posed a challenge
in accurately guessing the incorrect spelling of words. To address this
limitation, we propose a novel real-time finger spelling system based on the
YOLOv5 architecture. Our system employs specified rules and numerical classes
as triggers to efficiently generate hidden and compound characters, eliminating
the necessity for additional classes and significantly enhancing user
convenience. Notably, our approach achieves character spelling in an impressive
1.32 seconds with a remarkable accuracy rate of 98\%. Furthermore, our YOLOv5
model, trained on 9147 images, demonstrates an exceptional mean Average
Precision (mAP) of 96.4\%. These advancements represent a substantial
progression in augmenting BdSL interpretation, promising increased inclusivity
and accessibility for the linguistic minority. This innovative framework,
characterized by compatibility with existing YOLO versions, stands as a
transformative milestone in enhancing communication modalities and linguistic
equity within the Bangla Sign Language community.

提出了一种基于 YOLOv5 架构的新型实时手指拼写系统，通过使用指定规则和数字类作为触发器，高效生成隐藏和复合字符，大大提高了用户便利性，同时取得了令人瞩目的字符拼写准确率和模型平均精度，为增强 BdSL 解释提供了实质性的进展，提升了 Bangla 手语社区的沟通方式和语言平等性。