The impact of OCR accuracy on automated cancer classification of pathology reports  :0,evaluate the effects of Optical Character Recognition (OCR) on the automatic cancer classification of pathology  :16,images of pathology reports were converted to electronic freetext using a commercial OCR system. A stateoftheart cancer classification system, the Medical Text Extraction (MEDTEX) system, was used to automatically classify the OCR reports. Classifications produced by MEDTEX on the OCR versions of the reports were compared with the classification from a human amended version of the OCR  :74,employed OCR system was found to recognise scanned pathology reports with up to 99.12% character accuracy and up to 98.95% word accuracy. Errors in the OCR processing were found to minimally impact on the automatic classification of scanned pathology reports into notifiable groups. However, the impact of OCR errors is not negligible when considering the extraction of cancer notification items, such as primary site, histological type,  automatic cancer classification system used in :141,this work, MEDTEX, has proven to be robust to errors produced by the acquisition of freetext pathology reports from scanned :16,images through OCR software. However, issues emerge when considering the extraction of cancer notification items.

A Cost Efficient Approach to Correct OCR Errors in Large Document Collections Word error rate of an OCR is often higher than its character error rate. This is especially true when OCRs are designed by recognizing characters. High word accuracies are critical for many practical applications like content creation and texttospeech systems. In order to detect and correct the misrecognised words, it is common for an OCR to employ a postprocessor module to improve the word accuracy. However, conventional approaches to postprocessing like looking up a dictionary or using a statistical language model (SLM), are still limited. In many such scenarios, it is often required to remove the outstanding errors manually. We observe that the traditional postprocessing schemes look at error words sequentially, since OCRs process documents one at a time. We propose a costefficient model to address the error words in batches rather than correcting them individually. We exploit the fact that a collection of documents (eg. a book), unlike a single document, has a structure leading to repetition of words. Such words, if efficiently grouped together and corrected together, can lead to a significant reduction in the effort. Error correction can be fully automatic or with a human in the loop. We compare the performance of our method with various baseline approaches including the case where all the errors are removed by a human. We demonstrate the efficacy of our solution empirically by reporting more than 70% reduction in the human effort with near perfect error correction. We validate our method on books in both English and Hind