PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System
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Cited by 22 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Optical character recognition (OCR) technology has been widely used in various scenes, as shown in Figure 1. Designing a practical OCR system is still a meaningful but challenging task. In previous work, considering the efficiency and accuracy, we proposed a practical ultra lightweight OCR system (PP-OCR), and an optimized version PP-OCRv2. In order to further improve the performance of PP-OCRv2, a more robust OCR system PP-OCRv3 is proposed in this paper. PP-OCRv3 upgrades the text detection model and text recognition model in 9 aspects based on PP-OCRv2. For text detector, we introduce a PAN module with large receptive field named LK-PAN, a FPN module with residual attention mechanism named RSE-FPN, and DML distillation strategy. For text recognizer, the base model is replaced from CRNN to SVTR, and we introduce lightweight text recognition network SVTR LCNet, guided training of CTC by attention, data augmentation strategy TextConAug, better pre-trained model by self-supervised TextRotNet, UDML, and UIM to accelerate the model and improve the effect. Experiments on real data show that the hmean of PP-OCRv3 is 5% higher than PP-OCRv2 under comparable inference speed. All the above mentioned models are open-sourced and the code is available in the GitHub repository PaddleOCR which is powered by PaddlePaddle.
Key facts. Cited by 106 papers in the map (data through 14 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 7 Jun 2022.
Builds on
15 of 22 references.
References · 15
- 2017Squeeze-and-Excitation Networkscited by 4,789
- 2018Path Aggregation Network for Instance Segmentationcited by 798
- 2017Deep Mutual Learningcited by 628
- 2015An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioncited by 520
- 2021ICDAR2019 Competition on Scanned Receipt OCR and Information Extractioncited by 256
- 2019Real-time Scene Text Detection with Differentiable Binarizationcited by 214
- 2020PP-OCR: A Practical Ultra Lightweight OCR Systemcited by 151
- 2019ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019cited by 139
- 2017ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)cited by 105
- 2022SVTR: Scene Text Recognition with a Single Visual Modelcited by 88
- 2021What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labelscited by 63
- 2020GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognitioncited by 52
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Citing papers per year: 2023: 8; 2024: 30; 2025: 42; 2026: 26.
106 papers.
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