Big Data Analytics for Historical Document Processing

Abstract

Historical Document Processing is the process of digitizing written material from the past for future use by historians and other scholars. It incorporates algorithms and software tools from various subfields of computer science, including computer vision, document analysis and recognition, natural language processing, and machine learning, to convert images of ancient manuscripts, letters, diaries, and early printed texts automatically into a digital format usable in information retrieval systems. Within the past twenty years, as libraries, museums, and other cultural heritage institutions have scanned an increasing volume of their historical document archives, the need to transcribe the full text from these collections has become acute. Big Data Analytics and infrastructure will be essential tools in this field. This study compares performance analysis of two OCR systems, discusses an Historical Document Processing (HDP) workflow, and highlights the role of OCR software in a RESTful API for an HDPaaS (HDP as a Service) system.