Evaluation of Binarization Algorithms for Camera-Based Devices

  • M. Nava-Ortiz
  • W. Gómez-Flores
  • A. Díaz-Pérez
  • G. Toscano-Pulido
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)


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.


local adaptive binarization optical character recognition camera-based devices feature selection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. Nava-Ortiz
    • 1
  • W. Gómez-Flores
    • 1
  • A. Díaz-Pérez
    • 1
  • G. Toscano-Pulido
    • 1
  1. 1.Information Technology LaboratoryCINVESTAV-IPNMexico

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