An Optimization Scheme for College Teacher Recruitment Management System Based on Blockchain and Text Recognition

Abstract

At present, some colleges and universities use the recruitment system to complete the registration and review of candidates in the recruitment work, but the review work still requires reviewers to spend a lot of time to complete. In addition, candidates submit malicious incorrect registration information to disrupt recruitment, which not only increases the workload of reviewers, but also affects the efficiency of recruitment. Candidates need to upload scanned copies of certification materials (such as ID card, degree certificate, graduation certificate, etc.) when registering. Some candidates are worried about whether the information will be leaked and the reviewers are unfair in reviewing for personal gains. In response to these problems, this article proposes to add text recognition to the recruitment management system of a university teacher to complete the system's automatic registration review. Based on the traceability and non-tampering characteristics of blockchain technology, the security and traceability of registration information are guaranteed. Kind of optimization plan. By analyzing the system process, selecting a third-party API interface to complete text recognition, and combining the simple filtering function of the system to build an automated audit function of the system. Select the candidate's registration information as the traceability object, optimize the system process, write smart contracts, web3.js connect the system and the blockchain. Experiments have proved that the automated audit function is automatically executed after candidates submit the registration form, and the average audit speed is 550ms. Reduce the workload of reviewers and improve recruitment efficiency. If the candidate has questions about the review process, the candidate can retrospectively verify whether the registration information has been tampered with through the blockchain, which ensures the fairness of the recruitment work.