Invoice Detection Computer Vision Project
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This Invoice Detection project focuses on using machine learning and computer vision techniques to automatically extract critical data from invoices. The goal is to accurately detect and parse elements from invoices such as supplier information, client details, product descriptions, pricing, tax, and shipping information. The project leverages object detection models to identify and extract these key pieces of information, making invoice processing more efficient and automated.
The project consists of an object detection model trained on a dataset of invoices, with the following classes:
- Discount_Percentage
- Due_Date
- Email_Client
- Name_Client
- Products
- Remise (Discount)
- Subtotal
- Tax
- Tax_Percentage
- Tel_Client
- Billing Address
- Invoice Date
- Invoice Number
- Shipping Address
- Supplier Address
- Supplier Name
- Supplier Phone
- Total
Features:
- Custom Object Detection Model Detects and extracts multiple fields from invoices, allowing businesses to automate invoice data entry and processing.
- Automated Invoice Parsing Streamlines the extraction of key information for faster processing and integration into financial systems.
- Multiple Classes The model can classify a wide range of fields, including client details, product information, and financial figures.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
invoice-detection-tmti8_dataset,
title = { Invoice Detection Dataset },
type = { Open Source Dataset },
author = { Hemant Ramphul },
howpublished = { \url{ https://universe.roboflow.com/hemant-ramphul-wfioe/invoice-detection-tmti8 } },
url = { https://universe.roboflow.com/hemant-ramphul-wfioe/invoice-detection-tmti8 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { nov },
note = { visited on 2024-12-19 },
}