Table Extraction with Table Data Using VGG-19 Deep Learning Model
In recent years, significant progress has been achieved in understanding and processing tabular data. However, existing approaches often rely on task-specific features and model architectures, posing challenges in accurately extracting table structures amidst diverse layouts, styles, and noise conta...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/1/203 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548960811974656 |
|---|---|
| author | Muhammad Zahid Iqbal Nitish Garg Saad Bin Ahmed |
| author_facet | Muhammad Zahid Iqbal Nitish Garg Saad Bin Ahmed |
| author_sort | Muhammad Zahid Iqbal |
| collection | DOAJ |
| description | In recent years, significant progress has been achieved in understanding and processing tabular data. However, existing approaches often rely on task-specific features and model architectures, posing challenges in accurately extracting table structures amidst diverse layouts, styles, and noise contamination. This study introduces a comprehensive deep learning methodology that is tailored for the precise identification and extraction of rows and columns from document images that contain tables. The proposed model employs table detection and structure recognition to delineate table and column areas, followed by semantic rule-based approaches for row extraction within tabular sub-regions. The evaluation was performed on the publicly available Marmot data table datasets and demonstrates state-of-the-art performance. Additionally, transfer learning using VGG-19 is employed for fine-tuning the model, enhancing its capability further. Furthermore, this project fills a void in the Marmot dataset by providing it with extra annotations for table structure, expanding its scope to encompass column detection in addition to table identification. |
| format | Article |
| id | doaj-art-fc2e12a275ab469a9d9fad20c3b7d44e |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-fc2e12a275ab469a9d9fad20c3b7d44e2025-01-10T13:21:13ZengMDPI AGSensors1424-82202025-01-0125120310.3390/s25010203Table Extraction with Table Data Using VGG-19 Deep Learning ModelMuhammad Zahid Iqbal0Nitish Garg1Saad Bin Ahmed2Faculty of Science and Environmental Studies, Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, CanadaFaculty of Science and Environmental Studies, Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, CanadaFaculty of Science and Environmental Studies, Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, CanadaIn recent years, significant progress has been achieved in understanding and processing tabular data. However, existing approaches often rely on task-specific features and model architectures, posing challenges in accurately extracting table structures amidst diverse layouts, styles, and noise contamination. This study introduces a comprehensive deep learning methodology that is tailored for the precise identification and extraction of rows and columns from document images that contain tables. The proposed model employs table detection and structure recognition to delineate table and column areas, followed by semantic rule-based approaches for row extraction within tabular sub-regions. The evaluation was performed on the publicly available Marmot data table datasets and demonstrates state-of-the-art performance. Additionally, transfer learning using VGG-19 is employed for fine-tuning the model, enhancing its capability further. Furthermore, this project fills a void in the Marmot dataset by providing it with extra annotations for table structure, expanding its scope to encompass column detection in addition to table identification.https://www.mdpi.com/1424-8220/25/1/203table extraction modelinformation extractionconvolutional neural networkdeep neural network |
| spellingShingle | Muhammad Zahid Iqbal Nitish Garg Saad Bin Ahmed Table Extraction with Table Data Using VGG-19 Deep Learning Model Sensors table extraction model information extraction convolutional neural network deep neural network |
| title | Table Extraction with Table Data Using VGG-19 Deep Learning Model |
| title_full | Table Extraction with Table Data Using VGG-19 Deep Learning Model |
| title_fullStr | Table Extraction with Table Data Using VGG-19 Deep Learning Model |
| title_full_unstemmed | Table Extraction with Table Data Using VGG-19 Deep Learning Model |
| title_short | Table Extraction with Table Data Using VGG-19 Deep Learning Model |
| title_sort | table extraction with table data using vgg 19 deep learning model |
| topic | table extraction model information extraction convolutional neural network deep neural network |
| url | https://www.mdpi.com/1424-8220/25/1/203 |
| work_keys_str_mv | AT muhammadzahidiqbal tableextractionwithtabledatausingvgg19deeplearningmodel AT nitishgarg tableextractionwithtabledatausingvgg19deeplearningmodel AT saadbinahmed tableextractionwithtabledatausingvgg19deeplearningmodel |