Optical Character Recognition

Optical Character Recognition (OCR) aims to automatically convert images of text into machine-readable text, facilitating efficient document processing and information extraction. Current research emphasizes improving OCR accuracy, particularly for challenging scenarios like historical documents, low-resolution images, and complex layouts, often employing transformer-based language models and convolutional neural networks for both character recognition and post-processing error correction. These advancements are crucial for digitizing historical archives, enhancing accessibility to information, and automating various tasks across diverse fields, from document management to scientific literature analysis.