PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System

TL;DR

PP-OCRv3 significantly enhances lightweight OCR performance by upgrading detection and recognition models with novel modules, strategies, and training techniques, achieving 5% higher accuracy than PP-OCRv2 while maintaining efficiency.

Contribution

The paper introduces PP-OCRv3, a more robust and accurate lightweight OCR system with multiple model improvements and training strategies over previous versions.

Findings

PP-OCRv3 achieves 5% higher hmean than PP-OCRv2.

Introduces LK-PAN and RSE-FPN modules for better text detection.

Employs SVTR LCNet and TextConAug for improved text recognition.

Abstract

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.