Abstract
In the study of letter recognition, the recognition accuracy is impacted by fonts and styles, which is the main bottleneck that the technology is applied. In order to enhance the accuracy, a letter recognition algorithm based artificial immune, referred to as LEBAI, is presented. Inspired by nature immune system, antibody cell (B-cell) population is evolved until the B-cell population is convergent through the learning of each training antigen and the memory cells pool is updated by the optimal B-cell. Finally, recognition is accomplished by memory cells. It is tested by the well-known letter recognition data set of UCI (University of California at Irvine). Compared with HSAC (Letter Recognition Using Holland-Style Adaptive Classifiers), LEBAI showed that recognition accuracy is increased from 82.7% to 95.58%. LEBAI achieves the same recognition accuracy for the letters of different fonts and styles, or stretched and distorted randomly.
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Liang, C., Peng, L., Hong, Y., Wang, J. (2009). An English Letter Recognition Algorithm Based Artificial Immune. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_40
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DOI: https://doi.org/10.1007/978-3-642-01513-7_40
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