LayoutLMv3-finetuned-invoice Open-source Model - Free to Extract Key Invoice Information!

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Layoutlmv3 Finetuned Invoice

Developed by Theivaprakasham

A version of LayoutLMv3-base fine-tuned on an invoice dataset for invoice information extraction

Text Recognition

Transformers

#Invoice Information Extraction #High-precision OCR #Document Understanding

Downloads 896

Release Time : 6/7/2022

Model Overview

This model is based on Microsoft's LayoutLMv3 model, specifically fine-tuned for invoice information extraction tasks. It can predict key fields such as issuer name, address, postal code, date, amount, etc.

Model Features

High-precision Invoice Information Extraction

Achieves perfect performance with precision, recall, and F1 score all at 1.0 on the evaluation set.

End-to-End Processing Capability

Directly processes invoice images without requiring pre-OCR processing.

Multi-field Recognition

Can simultaneously recognize multiple fields such as issuer information, date, amount, etc.

Model Capabilities

Invoice Image Analysis

Structured Information Extraction

Text Recognition

Layout Analysis

Use Cases

Financial Automation

Automatic Invoice Entry System

Automatically extracts key information from uploaded invoice images and enters it into the financial system.

Reduces manual entry errors and improves processing efficiency.

Expense Reimbursement Review

Automatically verifies the completeness and consistency of invoice information.

Improves review accuracy and reduces compliance risks.

Document Digitization

Historical Invoice Digitization and Archiving

Automatically extracts structured data from scanned paper invoices.

Facilitates subsequent queries and analysis.

🚀 LayoutLM-v3 model fine-tuned on invoice dataset

This model is a fine-tuned version of microsoft/layoutlmv3-base on the invoice dataset. It uses Microsoft’s LayoutLMv3 trained on the Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. Simply upload an image or use the example image below, and the results will show up in a few seconds.

📦 Installation

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💻 Usage Examples

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📚 Documentation

Model Information

Property	Details
Model Type	LayoutLM-v3 fine-tuned on invoice dataset
Training Data	Invoice dataset
Metrics	Precision: 1.0, Recall: 1.0, F1: 1.0, Accuracy: 1.0, Loss: 0.0012

Intended uses & limitations

More information needed.

Training and evaluation data

All the training codes are available from the below GitHub link.

https://github.com/Theivaprakasham/layoutlmv3

The model can be evaluated at the HuggingFace Spaces link:

https://huggingface.co/spaces/Theivaprakasham/layoutlmv3_invoice

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	100	0.0878	0.968	0.9817	0.9748	0.9966
No log	4.0	200	0.0241	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0186	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0184	0.9854	0.9574	0.9712	0.9956
0.1308	10.0	500	0.0121	0.972	0.9858	0.9789	0.9971
0.1308	12.0	600	0.0076	0.9939	0.9878	0.9908	0.9987
0.1308	14.0	700	0.0047	1.0	0.9959	0.9980	0.9996
0.1308	16.0	800	0.0036	0.9960	0.9980	0.9970	0.9996
0.1308	18.0	900	0.0045	0.9960	0.9980	0.9970	0.9996
0.0069	20.0	1000	0.0043	0.9960	0.9980	0.9970	0.9996
0.0069	22.0	1100	0.0016	1.0	1.0	1.0	1.0
0.0069	24.0	1200	0.0015	1.0	1.0	1.0	1.0
0.0069	26.0	1300	0.0014	1.0	1.0	1.0	1.0
0.0069	28.0	1400	0.0013	1.0	1.0	1.0	1.0
0.0026	30.0	1500	0.0012	1.0	1.0	1.0	1.0
0.0026	32.0	1600	0.0012	1.0	1.0	1.0	1.0
0.0026	34.0	1700	0.0011	1.0	1.0	1.0	1.0
0.0026	36.0	1800	0.0011	1.0	1.0	1.0	1.0
0.0026	38.0	1900	0.0011	1.0	1.0	1.0	1.0
0.002	40.0	2000	0.0011	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.20.0.dev0
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1

🔧 Technical Details

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📄 License

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