Document Classification- Layout LM Custom Dataset
LANCER
Lancer:
<= 2 resources OR <=3 weeks effort OR <=$3000 USD
Growth:2 - 4 resources AND upto 4 months OR
upto $30000 USD
Enterprise:Everything else
(Development)
Project Description
A tech-first enterprise focused on automating document workflows sought an AI-driven solution to classify scanned business documents with high accuracy. They required a layout-aware classification system built on real-world document structures. The engagement led to a robust AI model tailored to their use case, enhancing document intelligence capabilities.
Scope Of Work
The project aimed to solve unstructured document classification by training a layout-aware AI model. The client needed a system to detect and classify scanned files using visual and textual layout patterns. The solution focused on custom dataset integration, OCR preprocessing, and end-to-end model training.
Our Solution
To meet the client’s objective of intelligent document classification, a fine-tuned solution was built using LayoutLM, optimized for custom document layouts. Here's how it was executed:
- Custom OCR Integration: Combined Tesseract OCR and PyTesseract for accurate text extraction from .tiff files.
- Smart Annotation Parsing: Integrated structured .txt annotation files with the images to train on real document layouts.
- Fine-tuning the LayoutLM Model: Leveraged the Transformers and Datasets libraries to customize the LayoutLM model for the client’s unique document types.
- Training Pipeline on Jupyter: Enabled modular training in Jupyter notebooks.
- Prediction Engine: Developed a plug-and-play notebook for classifying new documents.
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