KEYWORDS: Optical character recognition, Tunable filters, Detection and tracking algorithms, Education and training, Java, Data processing, Design and modelling, Databases, Data modeling, Correlation coefficients
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 user-based 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.
The search function of shopping software is essential, and good recommendations can increase the user's desire to buy. The fuzzy search and image similarity search proposed in this paper is a new retrieval method built on Elasticsearch, which can speed up the search and improve the retrieval's correctness. Its support for various complex texts dramatically facilitates the development of this project. This search type is used in home shopping software to improve the user's comfort significantly. The text is developed and designed based on the Golang language, whose high concurrency and excellent library functions help implement the functionality extensively. The user side is presented as a WeChat applet, which lowers the threshold of use and increases the dependency of users. With Elasticsearch's support for multiple languages and its unique vector search and text embedding features, the system can train models such as Contrastive Language-Image Pretraining (CLIP) and Natural Language Processing (NLP) on different images and languages, improving the search's accuracy. For the model generated by the training, vector search is performed to achieve the purpose of the search, and finally, the search results are returned to the front-end applet page for exhibition.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.