sign-language-recognition

论文提出 Isharah-Selfie 数据集，这是一个完全通过前置智能手机自拍视角采集的大型阿拉伯手语（ArSL）数据集，包含 16 名签名者的 8,997 段视频和 1,493 个句子，用于连续手语识别与翻译，并揭示现有方法在自拍条件及未见句子泛化上的不足。

提出了一个用于印度手语识别的混合框架，结合运动学条件自适应GCN与距离感知弹性Transformer，并利用熵驱动SVD采样器提取关键帧，在自建的120类原生ISL数据集上达到89.8%的准确率。

The paper presents the first experiments on isolated sign language recognition for Icelandic Sign Language using a low-resource dataset, showing that cross-lingual transfer from other sign languages significantly improves performance.

SignX proposes a novel framework for continuous sign language recognition that unifies heterogeneous pose formats into a compact latent space and achieves state-of-the-art accuracy with 50× computational acceleration over pixel-space baselines.