Feature Request: Computer Vision Aided Invoice Reader and Parser with Notion Integration

[MaximilianMauroner]

ChatGPT response to prompt:

Computer vision aided invoice reader and parser that inserts the purchases and prices into notion (describe this in github issue format)

Description:
I would like to request a new feature for a computer vision aided invoice reader and parser that can integrate with Notion. This feature will allow users to quickly and easily extract purchase details from a scanned invoice and insert them into a Notion database.

The proposed functionality of this feature is as follows:

User scans an invoice with their device camera using the application
The computer vision aided invoice reader and parser processes the image to identify and extract purchase details such as product name and price.
The extracted data is then inserted into a Notion database table.
The application will also allow the user to edit or confirm the extracted data before it is inserted into the database.
The application will provide an option to categorize the invoice and add tags for easy searching and organization.
This feature would be extremely useful for individuals or businesses that need to track their expenses and purchases. The integration with Notion will allow for easy organization and retrieval of the data.

Thank you for considering this feature request.

[MaximilianMauroner]

Implementing a full-featured computer vision aided invoice reader and parser with Notion integration in Rust would be a significant undertaking that would require a significant amount of time and effort. Here are some high-level steps that could be taken to implement such a system:

Choose a Rust computer vision library/framework: There are several Rust libraries available that can help you with computer vision tasks such as image processing, object detection, and machine learning. Some popular Rust computer vision libraries/frameworks include OpenCV-rust, RustyCV, and TensorFlow Rust. Choose one that is suitable for your needs.

Train a machine learning model: In order to identify and extract purchase details from an invoice, a machine learning model would need to be trained on a dataset of labeled invoices. This dataset would need to be annotated with information such as product name, price, and other relevant details. Once the machine learning model is trained, it can be used to process new invoices and extract the relevant data.

Integrate with Notion API: Notion has a public API that can be used to programmatically create, read, update, and delete data in a Notion database. You would need to authenticate your application with Notion and use their API to insert the extracted purchase details into the appropriate Notion database.

Build a user interface: To make the application user-friendly, you would need to build a user interface that allows the user to take a picture of an invoice, view the extracted purchase details, edit or confirm the details, and insert them into the Notion database.

Test and iterate: Once the application is built, it would need to be thoroughly tested to ensure that it is functioning correctly and meeting the user's needs. Iterate on the application based on user feedback and refine the machine learning model as needed.

Note that this is a high-level overview of what would be involved in implementing a computer vision aided invoice reader and parser with Notion integration in Rust. The actual implementation would require more detailed planning and coding work.