KO
|
EN
gitlite — search
Search
#python
#java
#python3
#arduino
#golang
#machine-learning
#rust
#html
#flask
#javascript
#seismology
#nodejs
LineCounter
★ 17
Open GitHub ↗
No description available.
Download README (.md)
Explore Similar Repositories
Obsidian-Dark-Graphite-Pie-theme
:
custom-CSS for Obsidian app
TweetFromUnityWebGL
:
Mobile-ready script for tweeting from Unity WebGL.
auth-nestjs
:
🔐 Sistema de autenticação desenvolvido com NestJS
travel_anchors
:
travel anchors for modern versions (1.16+)
MdcMotion
:
No description available.
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
LineCounter
?
Download (.md)
# LineCounter: Learning Handwritten Text Line Segmentation by Counting <div align="center"> <img src="https://www.um.edu.mo/wp-content/uploads/2020/09/UM-Logo_V-Black-1024x813.png" width="30%"> </div> *** This is the official repo for LineCounter (ICIP 2021). For details of LineCounter, please refer to ``` @INPROCEEDINGS{9506664, author={Li, Deng and Wu, Yue and Zhou, Yicong}, booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, title={Linecounter: Learning Handwritten Text Line Segmentation By Counting}, year={2021}, volume={}, number={}, pages={929-933}, doi={10.1109/ICIP42928.2021.9506664}} ``` *** # Overview <img src="https://github.com/Leedeng/LineCounter/blob/master/image/Structure.png" width="80%"> # Dependency SauvolaNet is written in the TensorFlow. - TensorFlow-GPU: 1.15.0 Other versions might also work but are not tested. # Demo Download the repo and create the virtual environment by following commands ``` conda create --name LineCounter --file requirements.txt ``` Download [trained-model](https://drive.google.com/file/d/1fMUkyg67QLLzyDMkU1vgnsgDIKb9SPF9/view?usp=sharing) *** Put trained-model in **LineCounter/expts/Allx768xDnCNN/models/BF32:BLK5:BN0,0:M8:LArelu:LCbefore_decoder:SC0:DSdrop:USbilinear:BD1:N0.00:P1/** *** Then play with the provided ipython notebook. Alternatively, one may play with the inference code using this [google colab link](https://colab.research.google.com/drive/1v-h7eSNhxfCTqQZC_IPGEp_s-sfA6dxn?usp=sharing). # Datasets We do not own the copyright of the dataset used in this repo. Below is a summary table of the datasets used in this work along with a link from which they can be downloaded: | Dataset | URL | | ------------ | ------- | | ICDAR-HCS2013 | https://users.iit.demokritos.gr/~nstam/ICDAR2013HandSegmCont/ | | HIT-MW | http://space.hit.edu.cn/article/2019/03/11/10660 (Chinese) | | VML-AHTE | https://www.cs.bgu.ac.il/~berat/ | # Concat For any paper related questions, please feel free to contact leedengsh@gmail.com.
