Automatic Assessment of OCR Quality in Historical Documents

Mass digitization of historical documents is a challenging problem for optical character recognition (OCR) tools. Issues include noisy backgrounds and faded text due to aging, border/marginal noise, bleed-through, skewing, warping, as well as irregular fonts and page layouts. As a result, OCR tools often produce a large number of spurious bounding boxes (BBs) in addition to those that correspond to words in the document. This paper presents an iterative classification algorithm to automatically label BBs (i.e., as text or noise) based on their spatial distribution and geometry. The approach uses a rule-base classifier to generate initial text/noise labels for each BB, followed by an iterative classifier that refines the initial labels by incorporating local information to each BB, its spatial location, shape and size. When evaluated on a dataset containing over 72,000 manually-labeled BBs from 159 historical documents, the algorithm can classify BBs with 0.95 precision and 0.96 recall...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Anshul Gupta, Ricardo Gutierrez-Osuna, Matthew Christy, Boris Capitanu, Loretta Auvil, Liz Grumbach, Richard Furuta, Laura Mandell
• Keywords: AAAI 2015, Intelligent Agents