Page image classification for content-specific data processing
📰 ArXiv cs.AI
Learn to classify page images for content-specific data processing in digitization projects using AI techniques
Action Steps
- Collect a dataset of page images from historical documents
- Preprocess the images using techniques such as binarization and deskewing
- Train a convolutional neural network (CNN) to classify the page images into categories such as text, graphics, and layout
- Evaluate the performance of the CNN using metrics such as accuracy and F1-score
- Apply the trained model to classify new page images and enable content-specific data processing
Who Needs to Know This
Data scientists and researchers in humanities can benefit from this technique to efficiently process large archives of page images
Key Insight
💡 Automated page image classification can significantly reduce manual sorting and analysis time in digitization projects
Share This
Classify page images from historical documents using CNNs for efficient data processing #AI #digitization
Key Takeaways
Learn to classify page images for content-specific data processing in digitization projects using AI techniques
Full Article
Title: Page image classification for content-specific data processing
Abstract:
arXiv:2507.21114v3 Announce Type: replace-cross Abstract: Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges for manual sorting and analysis. These archives contain diverse content, including various text types (handwritten, typed, printed), graphical elements (drawings, maps, photos), and layouts (plain text, tables, forms). Efficiently processing this heterogeneous data requires automated methods to cat
Abstract:
arXiv:2507.21114v3 Announce Type: replace-cross Abstract: Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges for manual sorting and analysis. These archives contain diverse content, including various text types (handwritten, typed, printed), graphical elements (drawings, maps, photos), and layouts (plain text, tables, forms). Efficiently processing this heterogeneous data requires automated methods to cat
DeepCamp AI