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scene-text-detection-and-recognition-benchmark's Introduction

Scene-Text-Detection-and-Recognition-benchmark

Introduction

Scene Text Detection and Recognition is an open source scene text detection and recognition benchmark based on PyTorch

Major features

  • Various backbones and pretrained models
  • Large-scale training configs
  • High efficiency and extensibility

Requirements

This is my experiment eviroument

  • python3.6
  • pytorch1.6.0+cu101

Todo list

  • implement DB
  • implement CRNN
  • implement ABCNet

Dataset

Detection

Supported:

  • ICDAR15
  • ICDAR17MLT
  • sythtext800K
  • Total Text
  • CTW1500
  • 2019ArT

Recognition

model zoo

Supported text detection:

Supported text recognition:

Supported End to End:

Installation

Please refer to install.md for installation and dataset preparation.

Experiments

All models are trained in the same condition, and might not get the best result

The performance of EAST

Method Backbone Pretrain Resolution Dataset Precision Recall F-score FPS
EAST VGG16 - 512 ICDAR15 0.81 0.81 0.81 -
EAST VGG16 - 512 ICDAR17 0.72 0.61 0.66 -
EAST VGG16 SynthText 512 ICDAR15 0.82 0.824 0.822 -

The performance of PSENet

Method Backbone Pretrain Resolution Dataset Precision Recall F-score FPS
PSENet(1s) ResNet50 - 640*640 SynthText - - - -
PSENet(1s) ResNet50 - 640*640 ICDAR15 0.816 0.795 0.805 -
PSENet(1s) ResNet50 - 640*640 ICDAR17(val) 0.755 0.614 0.677 -
PSENet(1s) ResNet50 - 640*640 ICDAR17(test) 0.762 0.643 0.698 -
PSENet(1s) ResNet50 - 640*640 Total-Text 0.8255 0.7597 0.7913 3.0
PSENet(1s) ResNet50 SynthText 640*640 ICDAR15 0.864 0.835 0.850 -
PSENet(1s) ResNet50 SynthText+ICDAR17 640*640 ICDAR15 0.883 0.853 0.868 3.0
PSENet(1s) ResNet50 SynthText 640*640 Total-Text 0.834 0.781 0.807 3.0

links

EAST: https://github.com/SakuraRiven/EAST

PSENet: https://github.com/WenmuZhou/PSENet.pytorch

DB: https://github.com/WenmuZhou/DBNet.pytorch

https://github.com/WenmuZhou/PytorchOCR

Contact

Eamil: [email protected]

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