https://github.com/ht0710/receipt-information-extraction
Receipt-Information-Extraction
https://github.com/ht0710/receipt-information-extraction
deep-learning information-extraction machine-learning mc-ocr ocr-recognition receipt
Last synced: 2 months ago
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Receipt-Information-Extraction
- Host: GitHub
- URL: https://github.com/ht0710/receipt-information-extraction
- Owner: HT0710
- License: mit
- Created: 2023-02-24T20:51:10.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-26T13:02:11.000Z (6 months ago)
- Last Synced: 2025-04-26T14:19:18.142Z (6 months ago)
- Topics: deep-learning, information-extraction, machine-learning, mc-ocr, ocr-recognition, receipt
- Language: Python
- Homepage:
- Size: 5.44 GB
- Stars: 15
- Watchers: 1
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
#
Receipt Information Extraction (RIE)
**Project completed on 21/03/2023**
This project is inspired by MC-OCR, more information about this competition can access here: [Link](https://www.rivf2021-mc-ocr.vietnlp.com/)
---
## Table of Contents
- [Receipt Information Extraction (RIE)](#receipt-information-extraction-rie)
- [Table of Contents](#table-of-contents)
- [Introduction](#introduction)
- [Install](#install)
- [Version](#version)
- [Requirements](#requirements)
- [Usage](#usage)
- [One-time run](#one-time-run)
- [Each step run](#each-step-run)
- [1. Remove background](#1-remove-background)
- [2. Rotate](#2-rotate)
- [3. Extract information](#3-extract-information)
- [Results](#results)
- [Citations](#citations)
- [License](#license)
- [References](#references)
- [Contact](#contact)
## Introduction
Receipt Information Extraction (RIE) is a task that involves extracting structured data from unstructured receipts. The goal is to identify and extract key information such as date, time, total amount, tax amount, items purchased, etc. from receipts in various formats and languages. This task can be useful for applications such as expense management, accounting, fraud detection and analytics.
## Install
Download the project
1. Click here: [RIE](https://github.com/HT0710/Receipt-Information-Extraction/archive/refs/heads/main.zip)
2. Unzip the `main.zip` file
3. Navigate into the project folder:
```bash
cd path-to-the-project-folder
```
Clone with git: **(NOT RECOMMENDED)**
```bash
# very slow download speed
git clone https://github.com/HT0710/Receipt-Information-Extraction
cd Receipt-Information-Extraction
```
Using `wget` on Linux:
```bash
# faster download speed
wget https://github.com/HT0710/Receipt-Information-Extraction/archive/refs/heads/main.zip
unzip main.zip
cd Receipt-Information-Extraction-main
```
### Version
- **`Python 3.8`**
Using Conda:
```bash
# Installation: https://docs.conda.io/en/latest/miniconda.html
conda create -n rie python=3.8
conda activate rie
```
### Requirements
- rembg = 2.0.30
- torch = 1.11
- torchvision = 0.12
- opencv-python = 4.7.0.72
- scikit-learn = 1.2.1
- scikit-image = 0.19.3
- scipy = 1.9.3
- imutils = 0.5.4
- PyYAML = 6.0
- einops = 0.6.0
- gdown = 4.6.4
```bash
pip install -r requirements.txt
```
Note: CUDA is required if you want to use GPU. You can follow my instructions [here](https://gist.github.com/HT0710/639ec6ad96f9c46e0d209ba2e50ee168)
## Usage
### One-time run
Modify the configurations in `config.yaml` then run:
```bash
python run.py
```
With CLI:
```bash
python run.py -h
```
- -i: Image path or Folder path
- -o: Output folder path
- -g: Which gpu to run | 0 for cpu | -1 for all (Default: -1)
- -mp: Maximum of cpu can use | -1 for 80% of your cpu (Default: -1)
Caution: Using 100% of your cpu may crash your system!
**Example:**
```bash
python run.py -i data/test/test_1.jpg -o output -g 0 -mp 10
```
More configurations can access in `config.yaml`
### Each step run
#### 1. Remove background
- Remove the image background
- Input and output folder can be modify in `background_remove.py`
- Note: only run with folder input
Execute:
```bash
python background_remove.py
```
#### 2. Rotate
- Rotate horizontal, invert and align straight
- Input and output folder can be modify in `rotate.py`
- Note: only run with folder input
Execute:
```bash
python rotate.py
```
#### 3. Extract information
- Extract the receipt information
- Input and output can be modify in `extract_info.py`
- Note: only run with single image input, if you want extract a folder please use `run.py`
Execute:
```bash
python extract_info.py
```
## Results
## Citations
You can find the paper here: [RIE](https://github.com/HT0710/Receipt-Information-Extraction/tree/main/example/RIE.pdf)
## License
This project is licensed under the MIT License. See [LICENSE](https://github.com/HT0710/Receipt-Information-Extraction/blob/main/LICENSE) for more details.
## References
- [Rembg](https://github.com/danielgatis/rembg) - [danielgatis](https://github.com/danielgatis)
- [CRAFT-pytorch](https://github.com/clovaai/CRAFT-pytorch) - [clovaai](https://github.com/clovaai)
- [VietOCR](https://github.com/pbcquoc/vietocr) - [pbcquoc](https://github.com/pbcquoc)
- [PICK-pytorch](https://github.com/wenwenyu/PICK-pytorch) - [wenwenyu](https://github.com/wenwenyu)
## Contact
Open an issue: [New issue](https://github.com/HT0710/Receipt-Information-Extraction/issues/new)
Mail: pthung7102002@gmail.com
---