A Persian Continuous Sign Language Dataset

Sign language (SL) is the primary mode of communication within the Deaf community. Recent advances in deep learning have led to the development of various applications and technologies aimed at facilitating bidirectional communication between the Deaf and hearing communities. However, challenges remain in the availability of suitable datasets for deep learning-based models. Only a few public large-scale annotated datasets are available for sign sentences, and none exist for Persian Sign Language sentences. To address this gap, we have collected a large-scale dataset comprising 10,000 sign sentence videos corresponding to 100 Persian sign sentences. This dataset includes comprehensive annotations such as the bounding box of the detected hand, class labels, hand pose parameters, and heatmaps. A notable feature of the proposed dataset is that it contains isolated signs corresponding to the sign sentences within the dataset. To analyze the complexity of the proposed dataset, we present extensive experiments and discuss the results. More concretely, the results of the models in key sub-domains relevant to Sign Language Recognition (SLR), including hand detection, pose estimation, real-time tracking, and gesture recognition, have been included and analyzed. Moreover, the results of seven deep learning-based models on the proposed datasets have been discussed. Finally, the results of Sign Language Production (SLP) using deep generative models have been presented. We report the experimental results of these models from these sub-areas, showcasing their performance on the proposed dataset.