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PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks

Cheng Cui, Yubo Zhang, Ting Sun, Xueqing Wang, Hongen Liu, Manhui Lin, Yue Zhang, Tingquan Gao, Changda Zhou, Jiaxuan Liu, Zelun Zhang, Jing Zhang, Jun Zhang, Yi Liu

Abstract

The advent of "OCR 2.0" and large-scale vision-language models (VLMs) has set new benchmarks in text recognition. However, these unified architectures often come with significant computational demands, challenges in precise text localization within complex layouts, and a propensity for textual hallucinations. Revisiting the prevailing notion that model scale is the sole path to high accuracy, this paper introduces PP-OCRv5, a meticulously optimized, lightweight OCR system with merely 5 million parameters. We demonstrate that PP-OCRv5 achieves performance competitive with many billion-parameter VLMs on standard OCR benchmarks, while offering superior localization precision and reduced hallucinations. The cornerstone of our success lies not in architectural expansion but in a data-centric investigation. We systematically dissect the role of training data by quantifying three critical dimensions: data difficulty, data accuracy, and data diversity. 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.

PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks

Abstract

The advent of "OCR 2.0" and large-scale vision-language models (VLMs) has set new benchmarks in text recognition. However, these unified architectures often come with significant computational demands, challenges in precise text localization within complex layouts, and a propensity for textual hallucinations. Revisiting the prevailing notion that model scale is the sole path to high accuracy, this paper introduces PP-OCRv5, a meticulously optimized, lightweight OCR system with merely 5 million parameters. We demonstrate that PP-OCRv5 achieves performance competitive with many billion-parameter VLMs on standard OCR benchmarks, while offering superior localization precision and reduced hallucinations. The cornerstone of our success lies not in architectural expansion but in a data-centric investigation. We systematically dissect the role of training data by quantifying three critical dimensions: data difficulty, data accuracy, and data diversity. 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.

Paper Structure

This paper contains 23 sections, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Specialized Two-Stage OCR Systems
  4. Vision-Language Models for OCR
  5. Method
  6. Lightweight Model Architecture Design
  7. Text Detection.
  8. Text Recognition.
  9. Large-scale Data Curation and Optimization
  10. Data Difficulty
  11. Data Accuracy
  12. Data Diversity
  13. Experiments
  14. Ablation Studies on Data Curation Strategy
  15. The Impact of Data Difficulty
  16. ...and 8 more sections

Figures (3)

  • Figure 1: PP-OCRv5 vs. Vision-Language Models: 1-EditDist Accuracy and Parameter Size Comparison.
  • Figure 2: Overview of the PP-OCRv5 Framework: (a) Lightweight model architecture design; (b) Large-scale data construction and optimization strategies; (c) Distribution of training data across 22.6M samples; (d) Comparative performance of the PP-OCR series across multiple scenarios.
  • Figure 3: Comprehensive analysis of training data characteristics. Our key findings include: (a) A “sweet spot” of medium-difficulty data is most effective; (b) The model exhibits remarkable robustness to label noise; (c) Data diversity is paramount for performance; (d) Model accuracy scales nearly linearly with data quantity.