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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep neural network to extract intelligent information from invoice documents.
| Date | Stars |
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| 2026-07-31 | 2693 |
| 2026-08-01 | 2694 |
| 2026-08-02 | 2695 |
| 2026-08-03 | 2695 |
| 2026-08-05 | 2695 |
| 2026-08-06 | 2695 |
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 -------------------------------------------------------------------------------- Deep neural network to extract intelligent information from invoice documents. **TL;DR** * An easy to use UI to view PDF/JPG/PNG invoices and extract information. * Train custom models using the Trainer UI on your own dataset. * Add or remove invoice fields as per your convenience. * Save the extracted information into your system with the click of a button. :star: We appreciate your star, it helps! The InvoiceNet logo was designed by [Sidhant Tibrewal](https://www.linkedin.com/in/sidhant-tibrewal-864058148/). [Check out](https://www.behance.net/tiber_sid) his work for some more beautiful designs. ---  --- **DISCLAIMER**: Pre-trained models for some general invoice fields are not available right now but will soon be provided. The training GUI and data preparation scripts have been made available. Invoice documents contain sensitive information because of which collecting a sizable dataset has proven to be difficult. This makes it difficult for developers like us to train large-scale generalised models and make them available to the community. If you have a dataset of invoice documents that you are comfortable sharing with us, please reach out (<[email protected]>). We have the tools to create the first publicly-available large-scale invoice dataset along with a software platform for structured information extraction. --- ## Installation #### Ubuntu 20.04 InvoiceNet has been developed and tested on **Ubuntu 20.04** with **CUDA Version: 11.8**, **cuDNN version: 8.9.7**, and **Tensorflow v2.13.1**. To install InvoiceNet on Ubuntu, run the following commands: ```bash git clone https://github.com/naiveHobo/InvoiceNet.git cd InvoiceNet/ # Run installation script ./install.sh ``` The install.sh script will install all the dependencies, create a virtual environment, and install InvoiceNet in the virtual environment. To be able to use InvoiceNet, you need to source the virtual environment that the package was installed in. ```bash # Source virtual environment source env/bin/activate ``` #### Windows 10 The recommended way is to install InvoiceNet along with its dependencies in an Anaconda environment: ```bash git clone https://github.com/naiveHobo/InvoiceNet.git cd InvoiceNet/ # Create conda environment and activate conda create --name invoicenet python=3.7 conda activate invoicenet # Install InvoiceNet pip install . # Install poppler conda install -c conda-forge poppler ``` Some dependencies also need to be installed separately on Windows 10 before running InvoiceNet: - [Tesseract 5.0.0](https://github.com/UB-Mannheim/tesseract/wiki) - [ImageMagick 7.0.10](https://imagemagick.org/script/download.php#windows) - [Ghostscript 9.52](https://www.ghostscript.com/download/gsdnld.html) ## Data Preparation The training data must be arranged in a single directory. The invoice documents are expected be PDF files and each invoice is expected to have a corresponding JSON label file with the same name. Your training data should be in the following format: ``` train_data/ invoice1.pdf invoice1.json nike-invoice.pdf nike-invoice.json 12345.pdf 12345.json ... ``` The JSON labels should have the following format: ``` { "vendor_name":"Nike", "invoice_date":"12-01-2017", "invoice_number":"R0007546449", "total_amount":"137.51", ... other fields } ``` To begin the data preparation process, click on the "Prepare Data" button in the GUI or follow the instructions below if you're using the CLI. ## Add Your Own Fields To add your own fields to InvoiceNet, open **invoicenet/\_\_init\_\_.py**. There are 4 pre-defined field types: - **FIELD_TYPES["general"]** : General field like names, address, invoice number, etc. - **FIELD_TYPES["optional"]** : Optional fields that might not be present in all invoices. - **FIELD_TYPES["amount
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Sarthak Mittal · @Polybee-SG
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6be75faeff1dfec9, llm:Repository topics and description: 'information-extraction, invoice, invoice-parser, invoice-pdf, invoice-management, invoices' and README: 'Deep neural network to extract intelligent information from invoice documents', provides UI, training and data prep, uses Keras/TensorFlow.
matched fp:6be75faeff1dfec9, llm:Repository topics and description: 'information-extraction, invoice, invoice-parser, invoice-pdf, invoice-management, invoices' and README: 'Deep neural network to extract intelligent information from invoice documents', provides UI, training and data prep, uses Keras/TensorFlow.
matched fp:6be75faeff1dfec9, llm:Repository topics and description: 'information-extraction, invoice, invoice-parser, invoice-pdf, invoice-management, invoices' and README: 'Deep neural network to extract intelligent information from invoice documents', provides UI, training and data prep, uses Keras/TensorFlow.