Methods, apparatus, devices, and storage media for aggregating cross-page test questions

By acquiring test question images and using label coding to assess relevance, the problem of low accuracy in automatic clustering of cross-page test question elements was solved, achieving efficient and accurate automatic classification of test question elements.

CN122290156APending Publication Date: 2026-06-26HANGZHOU ZHIJUAN PLANET TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HANGZHOU ZHIJUAN PLANET TECHNOLOGY CO LTD
Filing Date
2026-05-26
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technologies cannot effectively perform automatic clustering of test item elements, especially in the case of cross-page scenarios, where clustering accuracy is low, and manual integration and conventional text matching methods suffer from low efficiency and insufficient accuracy.

Method used

By acquiring test question images, extracting text blocks and dividing them into text clusters, evaluating the relevance using label encoding, and combining the trained aggregation model for automatic clustering, the accurate classification of the same test question elements is ensured.

Benefits of technology

It achieves highly accurate automatic clustering of test question elements, solves the problem of clustering cross-page test question elements, improves clustering efficiency and accuracy, and reduces human error.

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Abstract

This application relates to the field of computer technology and discloses a method, apparatus, device, and storage medium for cross-page test question aggregation. The method includes acquiring multiple test question images, each image including test question elements; extracting text blocks from the test question images and grouping text blocks belonging to the same test question and the same test question element into a text cluster, obtaining a text cluster set; searching for a baseline text cluster and multiple candidate text clusters in the text cluster set, where candidate text clusters are those related to the baseline text cluster; generating label codes for each text cluster in the baseline and candidate text clusters based on the test question element to which the text cluster belongs, the text blocks within the text cluster, and the position of the text blocks in the test question images; and searching for text clusters belonging to the same test question as the baseline text cluster in the candidate text clusters based on the label codes, obtaining a test question text cluster set. The method of this application can achieve automatic clustering of test question elements with high clustering accuracy.
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