Design of an Intelligent Grading System Based on Artificial Intelligence
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Yuhao Zhang, Hong Zhou, Jiaqi Wu, Weiping Deng, Haibo Yi
Artificial intelligence has been widely applied in image recognition tasks, and its applications span across various domains. This paper presents an intelligent grading system based on artificial intelligence (AI) that significantly improves the efficiency and accuracy of grading standardized tests. The proposed system utilizes deep learning techniques, including convolutional neural networks (CNN) and recurrent neural networks (RNN), to recognize Chinese characters and words in test papers. The system involves collecting labeled images containing Chinese characters and words, preprocessing the images to remove noise and standardize their size and orientation, extracting features from the preprocessed images, and training the deep learning model using CNN and RNN architectures for character and word recognition. Various metrics were used to evaluate the performance of the trained model, which achieved high accuracy and precision in identifying characters and words. The proposed system has significant practical implications for automating the grading process and reducing human bias.
AI, intelligent grading system, education, web design