Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural network learning and expert systems
Neural network learning and expert systems
Classification of binary document images into textual or nontextual data blocks using network models
Machine Vision and Applications
Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms
Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms
Document Image Analysis: An Executive Briefing
Document Image Analysis: An Executive Briefing
Models and Algorithms for Duplicate Document Detection
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
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This paper describes document processing techniques used in ImageRefiner, the automatic image enhancement system developed by CACI International Inc. Though other methods are used in the system, we discuss two techniques that are novel and well tested or particularly important in the system. The first is a novel segmentation method that segments the text image file into "homogeneous" segments. The second is the use of a neural network to select the best transformation for each segment. Our experiments show that after applying the transformation selected by the neural network method to each specific segment, the fully processed images usually have more accurate OCR output. On average, the OCR accuracy for processed images is 35% better than the original images for a test set of Arabic files.