基于深度卷积递归神经网络架构的手写识别系统.zip

# Handwriting Recognition System This repository is the Tensorflow implementation of the Handwriting Recognition System described in [Handwriting Recognition of Historical Documents with Few Labeled Data](https://www.researchgate.net/publication/325993975_Handwriting_Recognition_of_Historical_Documents_with_Few_Labeled_Data) (please cite the paper if you use this code in your research paper). This code was also used for the baseline system in [Fine-tuning Handwriting Recognition systems with Temporal Dropout](https://www.researchgate.net/publication/348958179_Fine-tuning_Handwriting_Recognition_systems_with_Temporal_Dropout). This code is free for academic and research use. For commercial use of the code please contact [Edgard Chammas](mailto:contact@edgard.net). To help run the system, sample images from [ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset](https://scriptnet.iit.demokritos.gr/competitions/8/) are added. <img src="https://github.com/0x454447415244/HandwritingRecognitionSystem/raw/master/image.jpg" width="30%"> ## Configuration General configuration can be found in config.py CNN-specific architecture configuration can be found in cnn.py ## Training ``` python train.py ``` This will generate a text log file and a Tensorflow summary. <img src="https://github.com/0x454447415244/HandwritingRecognitionSystem/blob/master/TensorBoard.png" width="100%"> ## Decoding ``` python test.py ``` This will generate, for each image, the line transcription. The output will be written to decoded.txt by default. ``` python compute_probs.py ``` This will generate, for each image, the posterior probabilities at each timestep. Files will be stored in Probs by default. ## Dependencies - Tensorflow - OpenCV-Python ## Citation Please cite the following paper if you use this code in your research paper: ``` @inproceedings{chammas2018handwriting, title={Handwriting Recognition of Historical Documents with few labeled data}, author={Chammas, Edgard and Mokbel, Chafic and Likforman-Sulem, Laurence}, booktitle={2018 13th IAPR International Workshop on Document Analysis Systems (DAS)}, pages={43--48}, year={2018}, organization={IEEE} } ``` ## Acknowledgment We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. <img src="https://upload.wikimedia.org/wikipedia/sco/thumb/2/21/Nvidia_logo.svg/1280px-Nvidia_logo.svg.png" width="20%"> ## Contributions Feel free to send your pull request or open issues.