Master of Science (MS)
First Committee Member
Second Committee Member
Number of Pages
Use of Neural Networks to Predict OCR Accuracy investigates issues in developing an artificial neural network (ANN) based system for prediction of OCR accuracy from the image of a page. This work extends the work of Blando and Gonzalez in the following ways: enlarging training data, proposing new features, comparing different ANN architectures, and introducing a cross-validation learning algorithm; The following experiments were performed: comparison of 14 dimension feature metrics and 7 dimension feature metrics, comparison of an ANN trained with and without cross-validation, comparison of different neural network architectures, comparison of prediction capability of neural network and linear regression, comparison of the prediction capability of neural network using 14 dimension feature metrics and linear regression using reject markers. The results show that neural network can outperform linear regression if properly trained, and that the new feature metrics provide improved predictive ability.
Accuracy; Networks; Neural; OCR; Predict
Computer science; Artificial intelligence
University of Nevada, Las Vegas
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Wang, Ying, "Use of neural networks to predict Ocr accuracy" (1997). UNLV Retrospective Theses & Dissertations. 3391.