Author(s): Ahmed Al-Ghanimi

Abstract: Recognition of symbols and words is important today, and neural networks can be used to detect these symbols and words. The main problem addressed is the requirement for a dependable and effective system that can achieve high accuracy despite the presence of varied font styles and minimal training data. Through the results that we present in this research, we conclude that the results obtained are better than the results reached in previous research methods by combining the Grasshopper Optimization algorithm and the Propeller algorithm. Specifically, compared with existing methods, our method improves the accuracy by about 1.11% to 6.35% for uppercase letters and 3.3% to 7.5% for lowercase letters. We can say that the Grasshopper algorithm helps in identifying the initial ideal areas Also, through an algorithm Propeller, we can improve these areas optimizes, Through this, we can determine the maximum possible degree of accuracy in the neural network. We can say that mixing these systems leads to a more powerful and effective system for character recognition, especially with differences in fonts. Through the results that we were able to obtain, we notice many features and characteristics that can help us solve the problem at hand: Firstly, very high accuracy in recognizing uppercase and lowercase letters in relation to the English language and comparing them with currently available methods. Secondly, a great ability to adapt to various font styles and also the ability to maintain high accuracy with the data specified for training. This proposed method can also recognize the handwriting of small and large letters in the English language, as these results showed great accuracy and efficiency in character recognition. Through this, it can be more reliable in real-world scenarios with different fonts and available data.

DOI: 10.33545/27075923.2024.v5.i1a.60

Pages: 01-11 | Views: 157 | Downloads: 72

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