Improving Character Recognition Accuracy with Meta-Heuristic Algorithms (JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences, 2024)
BackgroundRecognition of symbols and words is crucial in today's digital age, with Artificial neural networks algorithms (ANN) playing a significant role in this domain. The primary challenge addressed in this research is the need for a reliable and efficient system capable of achieving high accuracy in character recognition, despite varied font styles and minimal training data.Materials and Methods:Our study demonstrates combining Artificial Neural Networks with two Meta-Heuristic include Grasshopper and Propeller algorithms significantly improves accuracy of character recognition system. Many pre-processing techniques applying in order to achieve optimal segmentation of these character. After that, twenty-seven statistical features such as geometric, shape and size are extracted for capital and small alphabet character. Back-Propagation Learning Algorithm (BP) was used for training the ANN, optimising its performance and fine-tuning internal parameters over 1200 iterations.Results:This hybrid approach achieves high accuracy, more than 93% in both capital and small alphabet characters. The evaluation algorithms give 0.90 %Sensitivity and 0.93%Specificity.Conclusions:According to evaluation algorithms, the combining Artificial Neural Networks with two Meta-Heuristic algorithms achieves high accuracy character recognition.