A Novel Neural Network Based Method Developed for Digit Recognition Applied to Automatic Speed Sign Recognition

This Paper presents a new hybrid technique for digit recognition applied to the speed limit sign recognition task. The complete recognition system consists in the detection and recognition of the speed signs in RGB images. A pretreatment is applied to extract the pictogram from a detected circular road sign, and then the task discussed in this work is employed to recognize digit candidates. To realize a compromise between performances, reduced execution time and optimized memory resources, the developed method is based on a conjoint use of a Neural Network and a Decision Tree. A simple Network is employed firstly to classify the extracted candidates into three classes and secondly a small Decision Tree is charged to determine the exact information. This combination is used to reduce the size of the Network as well as the memory resources utilization. The evaluation of the technique and the comparison with existent methods show the effectiveness.

@article{Rouabeh2016,
  title     = {A Novel Neural Network Based Method Developed for Digit Recognition Applied to Automatic Speed Sign Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {2},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Hanene Rouabeh and Chokri Abdelmoula and Mohamed Masmoudi},
  doi       = {10.14569/IJACSA.2016.070240},
  url       = {https://doi.org/10.14569/IJACSA.2016.070240}
}