International Journal of Advanced Culture Technology

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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Character Classification with Triangular Distribution

  • Received : 2019.05.06
  • Accepted : 2019.05.23
  • Published : 2019.06.30
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Abstract

Due to the development of artificial intelligence and image recognition technology that play important roles in the field of 4th industry, office automation systems and unmanned automation systems are rapidly spreading in human society. The proposed algorithm first finds the variances of the differences between the tile values constituting the learning characters and the experimental character and then recognizes the experimental character according to the distribution of the three learning characters with the smallest variances. In more detail, for 100 learning data characters and 10 experimental data characters, each character is defined as the number of black pixels belonging to 15 tile areas. For each character constituting the experimental data, the variance of the differences of the tile values of 100 learning data characters is obtained and then arranged in the ascending order. After that, three learning data characters with the minimum variance values are selected, and the final recognition result for the given experimental character is selected according to the distribution of these character types. Moreover, we compare the recognition result with the result made by a neural network of basic structure. It is confirmed that satisfactory recognition results are obtained through the processes that subdivide the learning characters and experiment characters into tile sizes and then select the recognition result using variances.

Keywords

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Figure 1. Flowchart

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Figure 2. Neural Network with 16 Input Nodes, 20 Hidden Nodes and 1 Output Node

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Figure 1. (continued) Flowchart

Table 1. Output of 10 Number of Experimental Characters Obtained by Execution of Character Classification Algorithm

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Table 2. Output of 100 Number of Learning Characters Obtained by Execution of Neural Network After 2,000,000 or More Number of Repetition of Learning

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Table 3. Output of 10 Number of Experimental Characters Obtained by Execution of Neural Network After 2,000,000 or More Number of Repetition of Learning

E1GMBY_2019_v7n2_209_t0003.png 이미지

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