Prediction of OCR accuracy using simple image features
نویسندگان
چکیده
A classifier for predicting the character accuracy achieved by any Optical Character Recognition (OCR) system on a given page is presented. This classifier is based on measuring the amount of white speckle, the amount of character fragments, and overall size information in the page. No output from the OCR system is used. The given page is classified as either “good” quality (i.e., high OCR accuracy expected) or “poor” (i.e., low OCR accuracy expected). Results of processing 639 pages show a recognition rate of approximately 85%. This performance compares favorably with the ideal-case performance of a prediction method based upon the number of reject-markers in OCR generated text.
منابع مشابه
Prediction of OCR Accuracy
The accuracy of all contemporary OCR technologies varies drastically as a function of input image quality [Rice 92, Rice 93, Chen 93, Rice 94]. Given high quality images, many devices consistently deliver output text in excess of 99% correct. For low quality images, even images which are easily read by a human, output accuracy is frequently below 90%. This extreme sensitivity to quality is well...
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