Ms. Li Jiao Profile

Li Jiao

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Proceedings Article | 9 January 2024 Paper

Research on automatic identification algorithm of invoice information

Li Jiao, Hui Li

Proceedings Volume 12969, 129690U (2024) https://doi.org/10.1117/12.3014488

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The invoice reimbursement process is very cumbersome and requires manual entry of key information in the invoice, which wastes a lot of manpower and time. Therefore, it is particularly important to design an algorithm for intelligent identification of invoice information. Traditional algorithms can identify information from scanned invoice images. However, since in our country, most of the invoice information is Chinese characters, the current recognition algorithm has a certain degree of difficulty in identifying Chinese characters, and garbled characters will appear. Therefore, this article combines the CTPN text detection algorithm with the DesNets text recognition algorithm, and uses this algorithm to detect and recognize text on the information extracted from the invoice area image. Experiments show that the model outperforms the comparison model, with a recognition accuracy of up to 99.79%.

Proceedings Article | 10 October 2023 Paper

Research on information recommendation method of online education platform based on user preference model

Hui Li, Li Jiao

Proceedings Volume 12799, 127992S (2023) https://doi.org/10.1117/12.3006307

KEYWORDS: Data modeling, Data processing, Design and modelling, Data storage, Modeling, Tunable filters, Systems modeling, Feature extraction, Data conversion, Online learning

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In response to the current online education platform cannot well recommend personalized content to users, this study is based on user preferences, by building a user preference algorithm model for user survey and recommending courses to users according to the model guidance. A user preference modeling that incorporates long and short-term memory networks and self-attentive algorithm models is designed. Considering the influence of time interval between user behaviors on user preferences, the LSTM algorithm model is improved by using time gates and cosine functions to obtain user long-term preferences from user historical behavior sequences, and then user short-term interests are obtained by modeling user current session behavior sequences by introducing temporal location encoding information between user behaviors in the self-attentive algorithm model. The experimental results show that the user satisfaction is good, which indicates that this user preference algorithm model has some validity.

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