Analysis of Image Pattern Classification using Hopfield Network on Letters and Thai Banknotes via MATLAB Implementation

This paper presents the analysis of Image Pattern Detection using the Hopfield algorithm. Initially two simple letter patterns, L and T are used for mathematical and graphical illustration of pattern classification using Hopfield Algorithm. Mathematical analysis with simple and comprehensive elaboration helps reader to better understand and implement the algorithm in its applications. The analysis is further extended for the patterns L, T, C, U and Y. For each of the patterns, the analysis is done for matrix size of 3 × 3, 5 × 5, 10 × 10 and 28 × 28 with the noise ranging from 10% to 80%. The result of comparative analysis done for different patterns, matrix sizes, presence of noise for the algorithm presented in this paper shows that the convergence ratio decreases with the increase in noise percentage. Additionally, this paper explains the affect of Hebbian learning rule in the convergence ratio of patterns. Finally, Hopfield algorithm is applied for the classification of 20 Baht and 50 Baht Thai banknotes. With image processing in MATLAB and application of Hopfield algorithm, the classification of banknotes is successfully done in the presence of different noise levels.