Arabic Text

Research on Arabic text focuses on overcoming the challenges posed by its cursive nature and contextual dependencies, primarily through advancements in optical character recognition (OCR) and handwriting recognition (HWR). Current efforts leverage deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), and transformer-based architectures, often incorporating attention mechanisms to improve accuracy and efficiency in tasks such as text segmentation, character/word recognition, and even biometric identification. These advancements have significant implications for digitizing historical manuscripts, improving language learning technologies, and enabling new applications in fields like banking and information retrieval.