https://github.com/forchapeatl/shop-receipt-information-extraction
https://github.com/forchapeatl/shop-receipt-information-extraction
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/forchapeatl/shop-receipt-information-extraction
- Owner: Forchapeatl
- Created: 2021-06-13T05:33:43.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-06-13T06:19:26.000Z (about 4 years ago)
- Last Synced: 2025-01-03T23:44:58.736Z (6 months ago)
- Language: Python
- Size: 1.15 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: Readme.md
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README
#
This project is for the SPIE paper - Novel Receipt Recognition with Deep Learning Algorithms.
In this paper, we propose an end-to-end novel receipt recognition system for capturing effective information from receipts (CEIR).
The CEIR has three parts: preprocess, detection, recognition.
## Introduction
In the preprocessing method, by converting the image to gray scale and obtaining the gradient with the Sobel operator, the outline of the receipt area is decided by morphological transformations with the elliptic kernel.
In text detection, the modified connectionist text proposal network to execute text detection.
The `pytorch` implementation of detection is based on [CTPN](https://github.com/WenmuZhou/ctpn.pytorch).
In text recognition, the convolutional recurrent neural network with the connectionist temporal classification with maximum entropy regularization as a loss function to update the weights in networks and extract the characters from receipt.
The `pytorch` implementation of recognition is based on [CRNN](https://github.com/meijieru/crnn.pytorch) and [ENESCTC](https://github.com/liuhu-bigeye/enctc.crnn).
We validate our system with the scanned receipts optical character recognition and information extraction (SROIE) [database](https://drive.google.com/drive/folders/1ShItNWXyiY1tFDM5W02bceHuJjyeeJl2).
## Dependency
Python 3.6.3
1. torch==1.4
2. torchvision
3. opencv-python
4. lmdb
## Prediction
1. Download pre-trained model from [Google Drive](https://drive.google.com/file/d/1hQzbaJgqnu5jNuv80MpXx2eEVOe3vK5O/view?usp=sharing) and put the file under `./detection/output/` folder.
2. Change the image name to `demo.jpg` in the CEIR folder.
* Run `python crop.py` for stage 1.
* Run `python detect.py` for stage 2.
* Run `python recognize.py` for stage 3.
3. The result will be saved in `./result/`.
## Training
0. Put dataset in `./dataset/train/image` and `./dataset/train/label`.
1. Preprocess parameters can be changed in `./preprocess/crop.py`.
2. In the detection part, the `./detection/config.py` is used for configuring. After that, run `python train.py` in the detection folder.
3. In recognition, you need to change trainroot and other parameters in `train.sh`, then run `sh train.sh` to train.