Context-Sensitive Error Correction: Using Topic Models to Improve OCR

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.