Advanced Table Detection Models
Client: Confidential Enterprise Client (Under NDA)
- Developed advanced table detection models to extract tabular data with precision and efficiency from diverse document types including invoices, reports, and contracts.
- Extensively tested architectures like TableNet, ResNet, and Microsoft’s transformer-based models before finalizing PaddleOCR with tuned hyperparameters for optimal results.
- Streamlined the process of table detection and data retrieval, providing clients with time-saving, user-friendly APIs that enhance operational efficiency.
- Emphasized a privacy-first design, ensuring secure data handling that complies with strict privacy standards.
- Enabled scalable table extraction solutions capable of processing large document volumes without compromising accuracy or speed.
Problem
- Manual extraction of tabular data from diverse documents was time-consuming and error-prone.
- Existing off-the-shelf models struggled to handle complex table structures in real-world scenarios.
- Lack of scalable solutions for high-volume document processing.
- Traditional table detection tools did not prioritize data privacy, raising compliance concerns.
Technologies Used
Python
OpenCV
PaddleOCR
Pytorch
TensorFlow
FastAPI
HuggingFace
Solution
- Evaluated and compared multiple architectures including TableNet, ResNet, and transformer-based models to identify the best approach for table detection.
- Fine-tuned PaddleOCR’s hyperparameters, optimizing sampling rates and thresholds to achieve high accuracy in recognizing tables from various document formats.
- Integrated a privacy-first API for seamless and secure data extraction, ensuring compliance with client-specific privacy standards.
- Built a scalable pipeline capable of processing millions of documents efficiently while maintaining consistent performance.
Impact Achieved with Our Solution
- Achieved a 90% reduction in manual data extraction time for enterprise clients, streamlining workflows significantly.
- Enhanced document processing accuracy, with up to 98% precision in detecting complex tables.
- Provided clients with insightful performance metrics, enabling data-driven decision-making and continuous improvement.
- Positioned the client as a leader in leveraging AI for document automation and tabular data extraction.
- Opened avenues for collaboration with industries requiring high-volume document processing and analysis.