An introduction to digital image processing
An introduction to digital image processing
Digital Image Processing
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality Evaluation of Document Segmentation Results
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Reading LCD/LED Displays with a Camera Cell Phone
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
License Plate Recognition Based on Genetic Algorithm
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
IBM Journal of Research and Development
A License Plate-Recognition Algorithm for Intelligent Transportation System Applications
IEEE Transactions on Intelligent Transportation Systems
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Segmentation is an important step within optical character recognition systems, since the recognition rates depends strongly on the accuracy of binarization techniques. Hence, it is necessary to evaluate different segmentation methods for selecting the most adequate for a specific application. However, when gold patterns are not available for comparing the binarized outputs, the recognition rates of the entire system could be used for assessing the performance. In this article we present the evaluation of five local adaptive binarization methods for digit recognition in water meters by measuring misclassification rates. These methods were studied due to of their simplicity to be implemented in based-camera devices, such as cell phones, with limited hardware capabilities. The obtained results pointed out that Bernsens method achieved the best recognition rates when the normalized central moments are employed as features.