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New research compares OCR-based VQA systems against end-to-end models under image degradation

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the effectiveness of text-centric Visual Question Answering (VQA) systems when images are degraded by common issues like blur or low resolution. The study compares modular OCR-based pipelines with an end-to-end vision-language model, finding that fine-tuned modular systems, particularly one using SA-DBNet with ResNet-18, achieve significantly higher accuracy. The research also highlights that traditional OCR error metrics are poor indicators of VQA performance, emphasizing the need for task-specific evaluations. AI

RANK_REASON The cluster contains a research paper detailing an empirical study and new findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research compares OCR-based VQA systems against end-to-end models under image degradation

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The cluster contains a research paper detailing an empirical study and new findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ritali Vatsi, Rachapudi Jagadeesh, Shruti Singh Baghel, Himani Sharma, Amit Shukla, Pawan Goyal ·

    Beyond OCR Accuracy: Text-Centric VQA Under Image Degradation with Modular and End-to-End

    arXiv:2609.13815v1 Announce Type: new Abstract: Text-centric Visual Question Answering (VQA) requires reading and reasoning over text embedded in images, a task made substantially harder when images suffer from real-world degradation such as motion blur, low resolution, or compre…

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