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Project Title
Advanced Image-PDF Data Extraction System
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Timeline:
3 Months
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Tech Stack:
Python
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Demo Link:
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Client Name:
Project Detail:
An innovative image analysis solution designed to efficiently extract relevant data from image-based PDFs. Despite challenges presented by image quality, multilingual content, and tabular formats, the system consistently delivers high accuracy. Furthermore, in instances of unprocessable images, alerts are dispatched for human review.
Problem Statement:
Image-based PDFs present an array of challenges for conventional data extraction methods, from varied image qualities to linguistic diversity and the intricacy of tabulated data. Manual data extraction from these PDFs is not scalable and is prone to errors.
Solution:
We developed an AI-powered model capable of reading and interpreting image PDFs, extracting desired data even in challenging conditions. The model harnesses advanced optical character recognition (OCR) techniques, multilingual support, and table parsing capabilities. For images beyond the model's processing capacity, an alert system ensures human intervention, thus guaranteeing data accuracy.
Features:
- Advanced OCR: Superior recognition of text within image-based PDFs.
- Multilingual Support: Efficient extraction from documents in various languages.
- Tabular Extraction: Precision in detecting and extracting tabulated data.
- Alert Mechanism: Systematic alerts for images requiring manual review.
Use Cases:
- Archival Data Migration: Extracting data from old, scanned documents for digital archival.
- Multilingual Research: Gathering data from diverse international research papers.
- Business Intelligence: Processing vendor invoices, contracts, and reports that come in image PDF formats.
Data Science Specific Points:
- Data Collection: Image PDFs were sourced from various domains to ensure the model was trained on a diverse dataset, covering a broad spectrum of image qualities, languages, and formats.
- Data Analysis: Utilized OCR for text recognition, coupled with neural network models to interpret and structure the extracted data, especially in tabulated formats.
- Results: Achieved a commendable 94% accuracy in data extraction from image PDFs. The system not only automates a significant portion of the extraction process but also ensures minimal error rates through its manual review alert system.
Technologies Used:
- OCR Tools: Tesseract, CV2
- AI/ML Frameworks: TensorFlow, PyTorch
- Language Processing: Google Cloud Translation API
- Data Processing: Python (Pandas, NumPy)
- Alert Systems: Slack API, Email API