LayoutLMv3-finetuned-SROIE Open-source Document Understanding Model - Extract Structured Information from Scanned Documents for Free

Layoutlmv3 Finetuned Sroie

Developed by Theivaprakasham

A document understanding model fine-tuned on the SROIE dataset based on Microsoft's LayoutLMv3-base model, excelling in extracting structured information from scanned documents

Text Recognition

Transformers

#Document Information Extraction #High-precision OCR #Structured Data Recognition

Downloads 409

Release Time : 6/7/2022

Model Overview

This model is specifically designed for document information extraction tasks, capable of identifying and classifying key fields in documents such as receipts (e.g., date, amount, merchant information)

Model Features

High-precision Document Understanding

Achieves 94% F1 score on the SROIE dataset, accurately identifying key information in receipts

Multimodal Processing Capability

Simultaneously processes text content and visual layout information to enhance document understanding

End-to-end Training

Supports direct information extraction from raw document images without complex preprocessing

Model Capabilities

Receipt Information Extraction

Document Entity Recognition

Structured Data Generation

Visual Text Understanding

Use Cases

Financial Automation

Receipt Information Digitization

Automatically extracts merchant, amount, date, and other information from scanned receipts

Accuracy exceeds 99%, with an F1 score of 94%

Document Processing

Invoice Information Extraction

Identifies key fields in invoices and generates structured data

🚀 layoutlmv3-finetuned-sroie

This model is a fine - tuned version of microsoft/layoutlmv3-base on the sroie dataset. It is designed for token classification tasks and has achieved excellent performance on the sroie dataset.

📚 Documentation

Model Information

Property	Details
Tags	generated_from_trainer
Datasets	sroie
Metrics	precision, recall, f1, accuracy

Evaluation Results

This model achieves the following results on the evaluation set:

Loss: 0.0426
Precision: 0.9371
Recall: 0.9438
F1: 0.9404
Accuracy: 0.9945

The detailed evaluation results are as follows:

Model Name	Task Name	Task Type	Dataset Name	Dataset Type	Dataset Args	Metric Name	Metric Type	Metric Value
layoutlmv3 - finetuned - sroie	Token Classification	token - classification	sroie	sroie	sroie	Precision	precision	0.9370529327610873
layoutlmv3 - finetuned - sroie	Token Classification	token - classification	sroie	sroie	sroie	Recall	recall	0.9438040345821326
layoutlmv3 - finetuned - sroie	Token Classification	token - classification	sroie	sroie	sroie	F1	f1	0.9404163675520459
layoutlmv3 - finetuned - sroie	Token Classification	token - classification	sroie	sroie	sroie	Accuracy	accuracy	0.9945347083116948

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: 5000

Training Results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.32	100	0.1127	0.6466	0.6102	0.6279	0.9729
No log	0.64	200	0.0663	0.8215	0.7428	0.7802	0.9821
No log	0.96	300	0.0563	0.8051	0.8718	0.8371	0.9855
No log	1.28	400	0.0470	0.8766	0.8595	0.8680	0.9895
0.1328	1.6	500	0.0419	0.8613	0.9128	0.8863	0.9906
0.1328	1.92	600	0.0338	0.8888	0.9099	0.8993	0.9926
0.1328	2.24	700	0.0320	0.8690	0.9467	0.9062	0.9929
0.1328	2.56	800	0.0348	0.8960	0.9438	0.9193	0.9931
0.1328	2.88	900	0.0300	0.9169	0.9460	0.9312	0.9942
0.029	3.19	1000	0.0281	0.9080	0.9452	0.9262	0.9942
0.029	3.51	1100	0.0259	0.9174	0.9438	0.9304	0.9945
0.029	3.83	1200	0.0309	0.9207	0.9532	0.9366	0.9944
0.029	4.15	1300	0.0366	0.9195	0.9388	0.9291	0.9940
0.029	4.47	1400	0.0302	0.9343	0.9424	0.9383	0.9949
0.0174	4.79	1500	0.0349	0.9142	0.9517	0.9326	0.9939
0.0174	5.11	1600	0.0327	0.9322	0.9510	0.9415	0.9950
0.0174	5.43	1700	0.0317	0.9215	0.9561	0.9385	0.9938
0.0174	5.75	1800	0.0385	0.9282	0.9316	0.9299	0.9940
0.0174	6.07	1900	0.0342	0.9235	0.9481	0.9357	0.9944
0.0117	6.39	2000	0.0344	0.9287	0.9474	0.9379	0.9944
0.0117	6.71	2100	0.0388	0.9232	0.9445	0.9338	0.9941
0.0117	7.03	2200	0.0325	0.9269	0.9496	0.9381	0.9949
0.0117	7.35	2300	0.0343	0.9225	0.9438	0.9330	0.9941
0.0117	7.67	2400	0.0372	0.9216	0.9481	0.9347	0.9944
0.0081	7.99	2500	0.0385	0.9192	0.9589	0.9386	0.9944
0.0081	8.31	2600	0.0376	0.9293	0.9467	0.9379	0.9944
0.0081	8.63	2700	0.0425	0.9261	0.9474	0.9366	0.9941
0.0081	8.95	2800	0.0407	0.9266	0.9452	0.9358	0.9941
0.0081	9.27	2900	0.0403	0.9280	0.9467	0.9372	0.9941
0.0055	9.58	3000	0.0364	0.9287	0.9474	0.9379	0.9948
0.0055	9.9	3100	0.0427	0.9122	0.9510	0.9312	0.9941
0.0055	10.22	3200	0.0394	0.9223	0.9488	0.9354	0.9943
0.0055	10.54	3300	0.0393	0.9247	0.9561	0.9401	0.9945
0.0055	10.86	3400	0.0413	0.9334	0.9496	0.9414	0.9945
0.0049	11.18	3500	0.0400	0.9290	0.9517	0.9402	0.9945
0.0049	11.5	3600	0.0412	0.9317	0.9539	0.9427	0.9945
0.0049	11.82	3700	0.0419	0.9314	0.9481	0.9397	0.9947
0.0049	12.14	3800	0.0452	0.9243	0.9503	0.9371	0.9941
0.0049	12.46	3900	0.0412	0.9334	0.9496	0.9414	0.9947
0.0039	12.78	4000	0.0438	0.9294	0.9481	0.9387	0.9941
0.0039	13.1	4100	0.0416	0.9326	0.9467	0.9396	0.9944
0.0039	13.42	4200	0.0418	0.9327	0.9488	0.9407	0.9948
0.0039	13.74	4300	0.0423	0.9345	0.9460	0.9402	0.9946
0.0039	14.06	4400	0.0419	0.9286	0.9467	0.9376	0.9947
0.0022	14.38	4500	0.0426	0.9371	0.9438	0.9404	0.9945
0.0022	14.7	4600	0.0424	0.9371	0.9445	0.9408	0.9947
0.0022	15.02	4700	0.0427	0.9372	0.9467	0.9419	0.9947
0.0022	15.34	4800	0.0431	0.9339	0.9460	0.9399	0.9945
0.0022	15.65	4900	0.0431	0.9346	0.9467	0.9406	0.9946
0.0015	15.97	5000	0.0434	0.9324	0.9445	0.9384	0.9945

Framework Versions

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

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