Prediction of OCR accuracy using a Neural Network

A method for predicting the accuracy achieved by an OCR system on an input image is presented. It is assumed that there is an ideal prediction function. A neural network is trained to estimate the unknown ideal function. In this project, multilayer perceptrons were trained to predict the character accuracy performance of two OCR systems using the backpropagation training method. The results show that this approach is sound. The feasibility of using an accuracy prediction system as a lter to discriminate good quality images (for OCR) from poor quality images (for manual keying) was also examined using a cost model of a large-scale document conversion process. Results show that a prediction system can reduce the total cost of converting a set of documents.