Introduction to InvoiceNet
InvoiceNet is an advanced deep neural network tool designed to intelligently extract information from invoice documents. Whether in PDF, JPG, or PNG formats, InvoiceNet streamlines the process of identifying and retrieving essential data from invoices, making it easier than ever to manage business paperwork.
Key Features
- User-Friendly Interface: InvoiceNet offers an intuitive UI that allows users to conveniently view invoice documents and extract necessary information without hassle.
- Custom Model Training: Users can utilize the Trainer UI to train models using their datasets, enabling personalized invoice processing tailored to specific business needs.
- Flexibility with Invoice Fields: The system provides the flexibility to add or remove invoice fields, which helps in aligning the extracted data with the unique requirements of various users.
- Easy Data Saving: Once the information is extracted, it can be seamlessly saved into a user’s system with just a click, ensuring efficiency and ease of use.
Installation
InvoiceNet is optimized for Ubuntu 20.04 and Windows 10 environments. The setup includes detailed installation instructions to guide users through setting up the software on their systems with minimal effort.
For Ubuntu 20.04, users should clone the repository and run the installation script. After setting up a virtual environment, they can activate it to start using InvoiceNet.
For Windows 10, it is recommended to use Anaconda to manage InvoiceNet's dependencies. Instructions for installing prerequisite software like Tesseract and ImageMagick are also provided.
Preparing Your Data
To train the InvoiceNet system, invoice documents must be organized in a specific directory format. Each invoice should have a corresponding JSON label file. This setup allows InvoiceNet to accurately train and predict information based on the provided datasets.
Customization and Flexibility
Users can customize fields by editing a specific Python file in the system codebase. This flexibility ensures the tool adapts to varying business needs by allowing the addition of fields like vendor name, invoice date, and more. Different field types such as general, optional, amount, and date are supported based on the nature of the data.
Using the GUI and CLI
InvoiceNet comes with a GUI for training models and extracting data. Commands to launch the Trainer and Extractor GUIs are simple, making the system accessible to users without a deep technical background. Similarly, a Command Line Interface (CLI) provides additional flexibility for users who prefer working with command scripts.
Training involves preparing data and running scripts to develop models that can later predict and extract data from new invoice documents.
Prediction allows users to extract specific fields from individual or multiple invoice files by placing them in a designated directory and running prediction scripts.
Acknowledgments and References
The InvoiceNet system builds on the foundational work of Rasmus Berg Palm and colleagues, as detailed in significant academic publications. For scientific use, proper citation of these references is encouraged.
InvoiceNet represents a robust solution for extracting and managing information from invoices, streamlining business operations by leveraging the power of neural networks.