Article: Automatic correction of subjective sentence similarity questions based on text image recognition Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.9 pp.63 - 83 Abstract: Subjective questions play a vital role in educational and vocational assessments, yet manual grading presents challenges to both efficiency and fairness. To address these challenges in sentence similarity tasks, this study proposed an automated correction method by leveraging text-image recognition. A hardware module for data acquisition via image capture was employed, and a VGGNet-based model was used for highly accurate text recognition. Building on the recognised text, a novel automatic grading approach was introduced that integrated a T5 pre-trained model with a pointer network within a 'pre-training + fine-tuning' paradigm. Experimental results demonstrated the effectiveness of the method, achieving a text recognition accuracy of 98.61%, with a low error rate of 2.87% and a processing time of 1.21 seconds. These findings highlight the potential of the system for reliable and efficient automated assessment. Inderscience Publishers - linking academia, business and industry through research

Title: Automatic correction of subjective sentence similarity questions based on text image recognition

Authors: Kaichen Tang; Xuyang Tu

Addresses: School of Education and Languages, Hong Kong Metropolitan University, Hong Kong, China ' School of Education and Languages, Hong Kong Metropolitan University, Hong Kong, China

Abstract: Subjective questions play a vital role in educational and vocational assessments, yet manual grading presents challenges to both efficiency and fairness. To address these challenges in sentence similarity tasks, this study proposed an automated correction method by leveraging text-image recognition. A hardware module for data acquisition via image capture was employed, and a VGGNet-based model was used for highly accurate text recognition. Building on the recognised text, a novel automatic grading approach was introduced that integrated a T5 pre-trained model with a pointer network within a 'pre-training + fine-tuning' paradigm. Experimental results demonstrated the effectiveness of the method, achieving a text recognition accuracy of 98.61%, with a low error rate of 2.87% and a processing time of 1.21 seconds. These findings highlight the potential of the system for reliable and efficient automated assessment.

Keywords: text image recognition; sentence similarity; subjective questions; automatic correction; VGGNet; T5 pre training model; pointer network.

DOI: 10.1504/IJBIDM.2026.154227

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.9, pp.63 - 83

Received: 31 Oct 2025
Accepted: 02 Feb 2026
Published online: 17 Jun 2026 *

Title: Automatic correction of subjective sentence similarity questions based on text image recognition

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