A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
Evaluating glyph binarizations based on their properties
Proceedings of the 2013 ACM symposium on Document engineering
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Image binarization has a large effect on the rest of the document image analysis processes in character recognition. Algorithm development is still a major focus of research. Evaluation of image binarization has been done by comparison of the result of OCR systems on images binarized by different methods. That has been criticized in that the binarization alone is not evaluated, but rather how it interacts with the downstream processes. Recently pixel accurate "ground truth" images have been introduced for use in binarization algorithm evaluation. This has been shown to be open to interpretation. The choice of binarization ground truth affects the binarization algorithm design, either directly if design is by automated algorithm trying to match the provided ground truth, or indirectly if human designers adjust their designs to perform better on the provided data. Three variations in pixel accurate ground truth were used to train a binarization classifier. The performance can vary significantly depending on choice of ground truth, which can influence binarization design choices.