ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911770786922496 |
|---|---|
| author | Akdoğan, Adem Kurt, Murat |
| author_facet | Akdoğan, Adem Kurt, Murat |
| contents | In this work, product tables in invoices are obtained autonomously via a deep learning model, which is named as ExTTNet. Firstly, text is obtained from invoice images using Optical Character Recognition (OCR) techniques. Tesseract OCR engine [37] is used for this process. Afterwards, the number of existing features is increased by using feature extraction methods to increase the accuracy. Labeling process is done according to whether each text obtained as a result of OCR is a table element or not. In this study, a multilayer artificial neural network model is used. The training has been carried out with an Nvidia RTX 3090 graphics card and taken $162$ minutes. As a result of the training, the F1 score is $0.92$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02246 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images Akdoğan, Adem Kurt, Murat Computer Vision and Pattern Recognition Artificial Intelligence Information Retrieval Machine Learning Neural and Evolutionary Computing In this work, product tables in invoices are obtained autonomously via a deep learning model, which is named as ExTTNet. Firstly, text is obtained from invoice images using Optical Character Recognition (OCR) techniques. Tesseract OCR engine [37] is used for this process. Afterwards, the number of existing features is increased by using feature extraction methods to increase the accuracy. Labeling process is done according to whether each text obtained as a result of OCR is a table element or not. In this study, a multilayer artificial neural network model is used. The training has been carried out with an Nvidia RTX 3090 graphics card and taken $162$ minutes. As a result of the training, the F1 score is $0.92$. |
| title | ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Information Retrieval Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2402.02246 |