← 研踪 PaperTrailCVF · CVPR 2026:cvf:dcd5de55ff95002bb6d9592c7ccb2d6f · 记录 #1
PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks
Cui, ChengZhang, YuboSun, TingWang, XueqingLiu, HongenLin, ManhuiZhang, YueGao, TingquanZhou, ChangdaLiu, JiaxuanZhang, ZelunZhang, JingZhang, JunLiu, Yi 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 03:29
摘要
The advent of "OCR 2.0" and large-scale vision-language models (VLMs) has set new benchmarks in text recogni- tion. However, these unified architectures often come with significant computational demands, challenges in precise text localization within complex layouts, and a propen- sity for textual hallucinations. Revisiting the prevailing notion that model scale is the sole path to high accu- racy, this paper introduces PP-OCRv5, a meticulously op- timized, lightweight OCR system with merely 5 million pa- rameters. We demonstrate that PP-OCRv5 achieves per- formance competitive with many billion-parameter VLMs on standard OCR benchmarks, while offering superior lo- calization precision and reduced hallucinations. The cor- nerstone of our success lies not in architectural expansion but in a data-centric investigation. We systematically dis- sect the role of training data by quantifying three critical dimensions: data difficulty, data accuracy, and data diver- sity. Our extensive experiments reveal that with a sufficient volume of high-quality, accurately labeled, and diverse data, the performance ceiling for traditional, efficient two- stage OCR pipelines is far higher than commonly assumed. This work provides compelling evidence for the viability of lightweight, specialized models in the large-model era and offers practical insights into data curation for OCR. The source code and models are publicly available at https: //github.com/PaddlePaddle/PaddleOCR.
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记录 #1 · 2026 · 最新观测版本
PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks
The advent of "OCR 2.0" and large-scale vision-language models (VLMs) has set new benchmarks in text recogni- tion. However, these unified architectures often come with significant computational demands, challenges in precise text localization within complex layouts, and a propen- sity for textual hallucinations. Revisiting the prevailing notion that model scale is the sole path to high accu- racy, this paper introduces PP-OCRv5, a meticulously op- timized, lightweight OCR system with merely 5 million pa- rameters. We demonstrate that PP-OCRv5 achieves per- formance competitive with many billion-parameter VLMs on standard OCR benchmarks, while offering superior lo- calization precision and reduced hallucinations. The cor- nerstone of our success lies not in architectural expansion but in a data-centric investigation. We systematically dis- sect the role of training data by quantifying three critical dimensions: data difficulty, data accuracy, and data diver- sity. Our extensive experiments reveal that with a sufficient volume of high-quality, accurately labeled, and diverse data, the performance ceiling for traditional, efficient two- stage OCR pipelines is far higher than commonly assumed. This work provides compelling evidence for the viability of lightweight, specialized models in the large-model era and offers practical insights into data curation for OCR. The source code and models are publicly available at https: //github.com/PaddlePaddle/PaddleOCR.
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