People with hearing impairments are found worldwide; therefore, the development of
effective local level sign language recognition (SLR) tools is essential. We conducted a …

Local Transformer-based classification models have recently achieved promising results
with relatively low computational costs. However, the effect of aggregating spatial global …

Visual temporal-aligned translation aims to transform the visual sequence into natural
words, including important applicable tasks such as lipreading and fingerspelling …

Vision Transformers has demonstrated competitive performance on computer vision tasks
benefiting from their ability to capture long-range dependencies with multi-head self …

Polyp segmentation is essential for accelerating the diagnosis of colon cancer. However, it is
challenging because of the diverse color, texture, and varying lighting effects of the polyps …

Object Detection with Transformers (DETR) and related works reach or even surpass the
highly-optimized Faster-RCNN baseline with self-attention network architectures. Inspired by …

Camera-based text entry using American Sign Language (ASL) fingerspelling has become
more feasible due to recent advancements in recognition technology. However, there are …

Continuous fingerspelling recognition from videos is paramount for real-time sign language
(SL) interpretation, enhancing accessibility. Despite deep learning progress, challenges …

We address the task of American Sign Language fingerspelling translation using videos in
the wild. We exploit advances in more accurate hand pose estimation and propose a novel …

The recognition of American Sign Language (ASL) fingerspelling through machine learning
has seen significant advancements over the past few years. This literature review explores …