Document Conversion

Document conversion research focuses on efficiently and accurately transforming diverse document formats, like PDFs, into structured, machine-readable data. Current efforts leverage deep learning models, particularly transformer-based architectures, for tasks such as layout analysis, table recognition, and optical character recognition (OCR), often incorporating optimized tokenization strategies for improved speed and accuracy. These advancements are crucial for enabling large-scale data processing and analysis across various fields, from scientific literature management to accessibility solutions for individuals with disabilities. The development of large, diverse datasets and efficient cloud-based deployment strategies are also key areas of ongoing investigation.