A Pytorch implementation of DB-Text paper

Make awesome things that matter.

Command

Train model

• Modify some configuration in config.yaml

make train

Test model

make test-all

Evaluate model

• For evaluation metric, please refer to MegReader repository

# iou-based Pascal
make ioueval

# overlap-based DetEval
make deteval

History (on TotalText dataset)

Train data

Test data

Test dataset (TotalText)

Heatmap	Polygon	Rotated rectangle

Text-line detection (the model trained on CTW1500 dataset)

Image origin	Text-line detected

Full pipeline

• Recognition model was trained on MJSynth and SynthText dataset

Metric evaluation (DetEval - P/R/HMean)

# for TotalText dataset
make deteval

ToDo

• [ ] Support datasets
  • [x] TotalText
  • [x] ICDAR2015
  • [x] SCUT-CTW1500
  • [x] MSRA-TD500
  • [ ] COCO-Text
  • [ ] Synthtext
  • [ ] ArT2019 (included Total-Text, SCUT-CTW1500 and Baidu Curved Scene Text dataset)
• [ ] Pytorch-lightning
• [x] Model serving with Torchserve
• [x] Metric callback (P/R/F1)
• [x] IoU-based metric (P/R/F1 - Pascal)
• [x] Overlap-based metric (P/R/F1 - DetEval)
• [ ] Model quantization
• [ ] Model pruning
• [ ] Docker / docker-compose
• [ ] ONNX, TensorRT
• [x] Text recognition model

Reference

I got a lot of code from DBNet.pytorch, thanks to @WenmuZhou

• Real-time Scene Text Detection with Differentiable Binarization
• Evaluation metrics
• DBNet.pytorch
• DBNet.keras
• Real-time-Text-Detection
• PSENet.pytorch
• deep-text-recognition-benchmark
• volksdep
• TedEval
• torch2trt