AMLD 2021 Workshop – No mercy for manual entry
Event details
| Date | 29.09.2021 |
| Hour | 13:00 › 17:00 |
| Location |
STCC
|
| Category | Conferences - Seminars |
⚠️ A valid COVID certificate must be presented on site to enter the event. ⚠️
Machine Learning (ML) is having a huge impact in the automation of tedious and repetitive tasks in several industries. In this workshop, we take the example of the digitalization of paper documents to show how standard ML techniques can have a big impact in this context.
Many institutions deal every day with a large number of paper documents (invoices, vouchers, …). These documents are often treated by employees with a high business knowledge and entered manually in a database or in another type of data storage. This is clearly an inefficient way to use resources. Therefore, the automation of this task is a priority to many institutions.
The goal of this workshop is to show you how ML can be used in the automation of such a process. You will see how to implement algorithms to:
• Classify scanned documents (using a CNN model implemented with fastai)
• Detect the position of a few fields within the document (using Detectron2)
• Extract the information from these fields (using Tesseract)
• Use the human feedback to improve the system performance (human-in-the-loop or HITL).
Machine Learning (ML) is having a huge impact in the automation of tedious and repetitive tasks in several industries. In this workshop, we take the example of the digitalization of paper documents to show how standard ML techniques can have a big impact in this context.
Many institutions deal every day with a large number of paper documents (invoices, vouchers, …). These documents are often treated by employees with a high business knowledge and entered manually in a database or in another type of data storage. This is clearly an inefficient way to use resources. Therefore, the automation of this task is a priority to many institutions.
The goal of this workshop is to show you how ML can be used in the automation of such a process. You will see how to implement algorithms to:
• Classify scanned documents (using a CNN model implemented with fastai)
• Detect the position of a few fields within the document (using Detectron2)
• Extract the information from these fields (using Tesseract)
• Use the human feedback to improve the system performance (human-in-the-loop or HITL).
Links
Practical information
- Informed public
- Registration required