WenmuZhou / Psenet.pytorch
Licence: gpl-3.0
A pytorch re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network
Stars: ✭ 416
Programming Languages
python3
1442 projects
Labels
Projects that are alternatives of or similar to Psenet.pytorch
Awesome Deep Text Detection Recognition
A curated list of resources for text detection/recognition (optical character recognition ) with deep learning methods.
Stars: ✭ 2,282 (+448.56%)
Mutual labels: text-detection, ocr
doctr
docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.
Stars: ✭ 1,409 (+238.7%)
Mutual labels: ocr, text-detection
Ocr.pytorch
A pure pytorch implemented ocr project including text detection and recognition
Stars: ✭ 196 (-52.88%)
Mutual labels: text-detection, ocr
Craft Pytorch
Official implementation of Character Region Awareness for Text Detection (CRAFT)
Stars: ✭ 2,220 (+433.65%)
Mutual labels: text-detection, ocr
React Native Tesseract Ocr
Tesseract OCR wrapper for React Native
Stars: ✭ 384 (-7.69%)
Mutual labels: text-detection, ocr
Adelaidet
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
Stars: ✭ 2,565 (+516.59%)
Mutual labels: text-detection, ocr
pytorch.ctpn
pytorch, ctpn ,text detection ,ocr,文本检测
Stars: ✭ 123 (-70.43%)
Mutual labels: ocr, text-detection
Craft keras
Keras implementation of Character Region Awareness for Text Detection (CRAFT)
Stars: ✭ 143 (-65.62%)
Mutual labels: text-detection, ocr
PSENet-Tensorflow
TensorFlow implementation of PSENet text detector (Shape Robust Text Detection with Progressive Scale Expansion Networkt)
Stars: ✭ 51 (-87.74%)
Mutual labels: ocr, text-detection
craft-text-detector
Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector
Stars: ✭ 151 (-63.7%)
Mutual labels: ocr, text-detection
Megreader
A research project for text detection and recognition using PyTorch 1.2.
Stars: ✭ 332 (-20.19%)
Mutual labels: text-detection, ocr
Chineseaddress ocr
Photographing Chinese-Address OCR implemented using CTPN+CTC+Address Correction. 拍照文档中文地址文字识别。
Stars: ✭ 309 (-25.72%)
Mutual labels: text-detection, ocr
East icpr
Forked from argman/EAST for the ICPR MTWI 2018 CHALLENGE
Stars: ✭ 154 (-62.98%)
Mutual labels: text-detection, ocr
Text Detection
Text detection with mainly MSER and SWT
Stars: ✭ 167 (-59.86%)
Mutual labels: text-detection, ocr
Tedeval
TedEval: A Fair Evaluation Metric for Scene Text Detectors
Stars: ✭ 143 (-65.62%)
Mutual labels: text-detection, ocr
East
A tensorflow implementation of EAST text detector
Stars: ✭ 2,804 (+574.04%)
Mutual labels: text-detection, ocr
Differentiablebinarization
DB (Real-time Scene Text Detection with Differentiable Binarization) implementation in Keras and Tensorflow
Stars: ✭ 106 (-74.52%)
Mutual labels: text-detection, ocr
Craft Remade
Implementation of CRAFT Text Detection
Stars: ✭ 127 (-69.47%)
Mutual labels: text-detection, ocr
vietnamese-ocr-toolbox
A toolbox for Vietnamese Optical Character Recognition.
Stars: ✭ 26 (-93.75%)
Mutual labels: ocr, text-detection
Text Detection Ctpn
text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network
Stars: ✭ 3,242 (+679.33%)
Mutual labels: text-detection, ocr
Shape Robust Text Detection with Progressive Scale Expansion Network
Requirements
- pytorch 1.1
- torchvision 0.3
- pyclipper
- opencv3
- gcc 4.9+
Update
20190401
- add author loss, the results are compared in Performance
Download
resnet50 and resnet152 model on icdar 2015:
-
bauduyun extract code: rxjf
Data Preparation
follow icdar15 dataset format
img
│ 1.jpg
│ 2.jpg
│ ...
gt
│ gt_1.txt
│ gt_2.txt
| ...
Train
- config the
trainroot
,testroot
in config.py - use following script to run
python3 train.py
Test
eval.py is used to test model on test dataset
- config
model_path
,data_path
,gt_path
,save_path
in eval.py - use following script to test
python3 eval.py
Predict
predict.py is used to inference on single image
- config
model_path
,img_path
,gt_path
,save_path
in predict.py - use following script to predict
python3 predict.py
Performance
ICDAR 2015
only train on ICDAR2015 dataset with single NVIDIA 1080Ti
my implementation with my loss use adam and warm_up
Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
---|---|---|---|---|
PSENet-1s with resnet50 batch 8 | 81.13 | 77.03 | 79.03 | 1.76 |
PSENet-2s with resnet50 batch 8 | 81.36 | 77.13 | 79.18 | 3.55 |
PSENet-4s with resnet50 batch 8 | 81.00 | 76.55 | 78.71 | 4.43 |
PSENet-1s with resnet152 batch 4 | 85.45 | 80.06 | 82.67 | 1.48 |
PSENet-2s with resnet152 batch 4 | 85.42 | 80.11 | 82.68 | 2.56 |
PSENet-4s with resnet152 batch 4 | 83.93 | 79.00 | 81.39 | 2.99 |
my implementation with my loss use adam and MultiStepLR
Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
---|---|---|---|---|
PSENet-1s with resnet50 batch 8 | 83.39 | 79.29 | 81.29 | 1.76 |
PSENet-2s with resnet50 batch 8 | 83.22 | 79.05 | 81.08 | 3.55 |
PSENet-4s with resnet50 batch 8 | 82.57 | 78.23 | 80.34 | 4.43 |
PSENet-1s with resnet152 batch 4 | 85.33 | 79.87 | 82.51 | 1.48 |
PSENet-2s with resnet152 batch 4 | 85.36 | 79.73 | 82.45 | 2.56 |
PSENet-4s with resnet152 batch 4 | 83.95 | 78.86 | 81.33 | 2.99 |
my implementation with author loss use adam and warm_up
Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
---|---|---|---|---|
PSENet-1s with resnet50 batch 8 | 83.33 | 77.75 | 80.44 | 1.76 |
PSENet-2s with resnet50 batch 8 | 83.01 | 77.66 | 80.24 | 3.55 |
PSENet-4s with resnet50 batch 8 | 82.38 | 76.98 | 79.59 | 4.43 |
PSENet-1s with resnet152 batch 4 | 85.16 | 79.87 | 82.43 | 1.48 |
PSENet-2s with resnet152 batch 4 | 85.03 | 79.63 | 82.24 | 2.56 |
PSENet-4s with resnet152 batch 4 | 84.53S | 79.20 | 81.77 | 2.99 |
my implementation with author loss use adam and MultiStepLR
Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
---|---|---|---|---|
PSENet-1s with resnet50 batch 8 | 83.93 | 79.48 | 81.65 | 1.76 |
PSENet-2s with resnet50 batch 8 | 84.17 | 79.63 | 81.84 | 3.55 |
PSENet-4s with resnet50 batch 8 | 83.50 | 78.71 | 81.04 | 4.43 |
PSENet-1s with resnet152 batch 4 | 85.16 | 79.58 | 82.28 | 1.48 |
PSENet-2s with resnet152 batch 4 | 85.13 | 79.15 | 82.03 | 2.56 |
PSENet-4s with resnet152 batch 4 | 84.40 | 78.71 | 81.46 | 2.99 |
official implementation use SGD and StepLR
Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
---|---|---|---|---|
PSENet-1s with resnet50 batch 8 | 84.15 | 80.26 | 82.16 | 1.76 |
PSENet-2s with resnet50 batch 8 | 83.61 | 79.82 | 81.67 | 3.72 |
PSENet-4s with resnet50 batch 8 | 81.90 | 78.23 | 80.03 | 4.51 |
PSENet-1s with resnet152 batch 4 | 82.87 | 78.76 | 80.77 | 1.53 |
PSENet-2s with resnet152 batch 4 | 82.33 | 78.33 | 80.28 | 2.61 |
PSENet-4s with resnet152 batch 4 | 81.19 | 77.13 | 79.11 | 3.00 |
examples
reference
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].