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How to Accurately Classify Documents with Intelligent OCR? A Concrete Use Case on ID Documents
Case study
Last update:
April 22, 2025
5 minutes
Quickly learn how to turn documents containing tables, line-by-line data, or other complex structures into data ready to be used in spreadsheets or Excel. Convert unstructured information into organized, actionable data.
Extracting tables from scanned documents is hard—manual entry or basic OCR often causes errors and slows down workflows.
Financial and accounting data is often buried in scattered tables within PDF files or images, making it difficult to access and analyze.
Thanks to artificial intelligence and optical character recognition (OCR) technologies, it is now possible to automatically extract and structure this information even when it is not available as selectable text.
Once extracted, this data can be organized in a way that maximizes its value, enabling cost savings, error detection, and more efficient expense management.
In this article, we explore the main techniques used to detect and extract tables from documents, along with practical tips to help your developers implement these solutions in your projects.
Today, it is possible to extract and structure data from these tables to maximize its use: opportunities for savings, error detection, expense management.
We present the main artificial intelligence techniques used to detect and extract tables from documents, along with practical tips to help your developers implement these solutions in your own projects.
Computer vision plays a crucial role in table detection. Common methods include the use of Convolutional Neural Networks (CNN) to identify tabular structures in documents. These networks can be trained on labeled datasets to learn how to recognize table borders and cells.
Key Technique: YOLO (You Only Look Once)
Once the tables are detected, the next step is their extraction and understanding. NLP techniques are used to interpret the data contained in the tables and to structure it in a usable manner.
Key Technique: Transformer Models (e.g., BERT, GPT)
Combining computer vision and NLP results in more robust outcomes. For example, a common approach is to use computer vision to detect tables and then apply NLP techniques to extract and structure the data.
Example of a Combined Approach at Koncile
The quality of training data is crucial for AI model performance. Ensure you have a diverse and well-labeled dataset. Include different types of documents and table formats to make your model more robust.
Separate your dataset into training and validation sets. Use cross-validation techniques to evaluate your models' performance and avoid overfitting.
After training your models, optimize them for production use. This may include compressing models to make them lighter and faster, as well as setting up robust infrastructures to handle real-time demands.
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