Intelligent learning application based on optical character recognition and personalized recommendation algorithms

Abstract

This paper proposes an intelligent learning application based on OCR (optical character recognition) text recognition and a personalized recommendation algorithm, which can help students to record their mistakes and recommend similar questions according to the type of mistakes so that students can further consolidate their mistakes and improve their correctness rate in their daily work. This paper is based on the JAVA language for development and design. The user side is presented in the form of a wechat applet, which reduces the use threshold and increases the user's dependability. The project combines the functional features of OCR algorithms for regular and irregular text recognition. It adds the ability to recognize distorted and blurred fonts to print fonts, scanned text, and other horizontally positioned text, making it more relevant to everyday use scenarios. The personalized recommendation algorithm of the project adopts various recommendation algorithms respectively, among which the collaborative recommendation algorithm is divided into userbased collaborative Filtering and item-based multi-criteria collaborative filtering, which perform relevant recommendations for user positive feedback exercises and the collaborative recommendation algorithm divided into user-based collaborative filtering and item-based multi-criteria collaborative filtering, which can predict users' preferences more accurately. The project also incorporates an exercise recommendation method that models the user's knowledge state, which models the user's personalized knowledge state based on their daily problem-solving habits, and makes exercise recommendations based on the model, which is a good predictor.