Use of neural networks to predict Ocr accuracy

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

Predictor of OCR accuracy using statistical techniques

Systems that predict optical character recognition (OCR) accuracy of an input image by a given OCR system were developed. Seven features associated with image defects were identified and utilized. Two kinds of nonparametric classification engines, the nearest neighbor rule-based and neural network-based, were implemented. The performance of these systems were compared to an old heuristic-based system using a cost model of a large-scale document conversion process and a test data set consisting of 502 pages. The results show that the performance of new classifiers were better than that of the heuristic-based system. The neural network-based system outperformed the nearest-neighbor-based system. These new systems can be used to reduce the cost of a large-scale document conversion process by discriminating good quality pages for OCR from degraded images for manual data entry

Évaluation de la qualité des documents anciens numérisés

Les travaux de recherche présentés dans ce manuscrit décrivent plusieurs apports au thème de l évaluation de la qualité d images de documents numérisés. Pour cela nous proposons de nouveaux descripteurs permettant de quantifier les dégradations les plus couramment rencontrées sur les images de documents numérisés. Nous proposons également une méthodologie s appuyant sur le calcul de ces descripteurs et permettant de prédire les performances d algorithmes de traitement et d analyse d images de documents. Les descripteurs sont définis en analysant l influence des dégradations sur les performances de différents algorithmes, puis utilisés pour créer des modèles de prédiction à l aide de régresseurs statistiques. La pertinence, des descripteurs proposés et de la méthodologie de prédiction, est validée de plusieurs façons. Premièrement, par la prédiction des performances de onze algorithmes de binarisation. Deuxièmement par la création d un processus automatique de sélection de l algorithme de binarisation le plus performant pour chaque image. Puis pour finir, par la prédiction des performances de deux OCRs en fonction de l importance du défaut de transparence (diffusion de l encre du recto sur le verso d un document). Ce travail sur la prédiction des performances d algorithmes est aussi l occasion d aborder les problèmes scientifiques liés à la création de vérités-terrains et d évaluation de performances.This PhD. thesis deals with quality evaluation of digitized document images. In order to measure the quality of a document image, we propose to create new features dedicated to the characterization of most commons degradations. We also propose to use these features to create prediction models able to predict the performances of different types of document analysis algorithms. The features are defined by analyzing the impact of a specific degradation on the results of an algorithm and then used to create statistical regressors.The relevance of the proposed features and predictions models, is analyzed in several experimentations. The first one aims to predict the performance of different binarization methods. The second experiment aims to create an automatic procedure able to select the best binarization method for each image. At last, the third experiment aims to create a prediction model for two commonly used OCRs. This work on performance prediction algorithms is also an opportunity to discuss the scientific problems of creating ground-truth for performance evaluation.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF