MBZUAI researchers have developed an AI program using vision transformers that can learn a person's handwriting style and generate text in that style. The US Patent and Trademark Office recently granted a patent for this technology, which could aid individuals with writing impairments. The system overcomes limitations of previous GAN-based approaches by processing long-range dependencies in handwriting. Why it matters: This patented AI tool enhances personalized text generation and has potential applications in assistive technology and improving handwriting recognition models.
Researchers from Alexandria University introduce AlexU-Word, a new dataset for offline Arabic handwriting recognition. The dataset contains 25,114 samples of 109 unique Arabic words, covering all letter shapes, collected from 907 writers. The dataset is designed for closed-vocabulary word recognition and to support segmented letter recognition-based systems. Why it matters: This dataset can help advance Arabic handwriting recognition systems, addressing a need for high-quality Arabic datasets in NLP research.
This paper introduces a novel dataset for Arabic handwritten isolated alphabet letters to serve as a benchmark for future research. The study presents a comparative evaluation of window-based descriptors for Arabic handwritten alphabet recognition, testing different descriptors with various classifiers. The experiments demonstrate that window-based descriptors perform well, especially when combined with a novel spatial pyramid partitioning scheme. Why it matters: The new dataset and analysis of descriptors will help advance Arabic OCR and handwritten text recognition systems.
This paper introduces DetectLLM-LRR and DetectLLM-NPR, two novel zero-shot methods for detecting machine-generated text using log rank information. Experiments across three datasets and seven language models demonstrate improvements of up to 3.9 AUROC points over state-of-the-art methods. The code and data for both methods are available on Github.
The paper introduces a two-step approach for transliterating Judeo-Arabic text (written in Hebrew script) into Arabic script. The method involves character-level mapping followed by post-correction to fix grammatical and orthographic errors. The authors also benchmarked LLMs on the transliteration task and demonstrate that transliteration enables the use of Arabic NLP tools on Judeo-Arabic. Why it matters: This work makes Judeo-Arabic texts more accessible to Arabic NLP, enabling processing and analysis that was previously impossible.
A proposed recognition system aims to identify missing persons, deceased individuals, and lost objects during the Hajj and Umrah pilgrimages in Saudi Arabia. The system intends to leverage facial recognition and object identification to manage the large crowds expected in the coming decade, estimated to reach 20 million pilgrims. It will be integrated into the CrowdSensing system for crowd estimation, management, and safety.