Efficient Information Extraction from Logistics Documents using OCR and AI Model: LayoutLMv3

In the logistics industry, the rapid and precise extraction of vital information from documents plays a pivotal role in optimizing operations and decision-making processes. This research focuses on the application of Optical Character Recognition (OCR) technologies and AI models like LayoutLMv3 to streamline and enhance the extraction of critical data from logistics documents. The study investigates the utilization of various OCR tools, including Tesseract, Google Vision OCR, and PaddlePaddle, in the context of logistics document processing. It explores the strengths and limitations of these OCR technologies and their compatibility with AI-driven information extraction methods. Our research methodology encompasses the development and optimization of AI models for the automated recognition of essential data fields, such as shipping details, product descriptions, and quantities, within unstructured logistics documents. Fine-tuning and performance evaluation are central to this investigation, with a focus on accuracy, recall, and overall efficiency. In addition to OCR and AI model development, this study explores strategies for the seamless integration of these technologies into logistics workflows. We emphasize the importance of user-friendly interfaces to facilitate interaction between logistics professionals and the AI-powered document processing system. The findings of this research hold the potential to significantly improve logistics document processing by reducing manual labor and enhancing data accuracy.