Context-Sensitive Error Correction: Using Topic Models to Improve OCR
Michael J. Wick, M. Ross, Erik Learned-Miller · Proceedings of the International Conference on Document Analysis and Recognition · 2007
Modern optical, character recognition software relies on human interaction to correct mis recognized characters. Even though the software often reliably identifies low-confidence output, the simple language and vocabulary models employed are insufficient to automatically correct mistakes. This paper demonstrates that topic models, which automatically detect and represent an article's semantic context, reduces error by 7% over a global word distribution in a simulated OCR correction task. Detecting and leveraging context in this manner is an important step towards improving OCR.