The field of sign language and text recognition is witnessing significant advancements, driven by innovative approaches and technologies. Researchers are exploring new methods to improve gaze awareness in remote sign language conversations, enabling more natural and immersive interactions. Meanwhile, the development of efficient and accurate text recognition systems is ongoing, with a focus on reducing prediction times and improving context modeling. The integration of artificial intelligence and machine learning techniques is also being investigated to enhance the capabilities of sign language and text recognition systems. Noteworthy papers in this area include those that propose novel architectures and techniques for sign language production, text detection, and recognition. Notable papers include: The See-Through Face Display for DHH People, which presents a sign language conversation system that enhances gaze awareness in remote interactions. The Meta-DAN paper, which proposes a novel decoding strategy to reduce prediction times in page-level handwritten text recognition. The VISTA-OCR paper, which introduces a lightweight and generative architecture for end-to-end OCR models.