High Accuracy Optical Character Recognition Using Neural Networks with Centroid Dithering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototype Extraction and Adaptive OCR
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text and Non-text Segmentation and Classification from Document Images
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
Robust Extraction of Text from Camera Images
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Hough Transform from the Radon Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localizing and segmenting text in images and videos
IEEE Transactions on Circuits and Systems for Video Technology
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Decision-based neural networks with signal/image classification applications
IEEE Transactions on Neural Networks
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The problem of Optical Character Recognition OCR is methodically treated in this paper. The paper discusses some traditional methods like template matching and neural networks that are employed by the more advanced and artificially intelligent systems. Though K-PCA, a non-intelligent system, provides better accuracy, it was found to occur at the cost of more processing time. Neural Networks provide a trade-off between processing time and accuracy.