Building a Python Web Scraping Tool for Document Data Extraction
Creating an effective web scraping solution requires careful planning and implementation, especially when dealing with large datasets. A recent project demonstrates how to build a Python-based scraper that can extract specific information from over 20,000 document records.
Project Overview
The task involved creating a Python script capable of automating data extraction from a website by inputting document numbers one by one, capturing specific information from the results page, and saving it in a structured format (CSV, Excel, or JSON).
Technical Implementation
The solution utilizes several powerful Python libraries:
- Playwright for headless browser automation
- Requests for API calls
- PDF Plumber for OCR and text extraction from images and PDFs
The developer chose PDF Plumber over alternatives like Tesseract and EasyOCR after comparison testing revealed it provided superior accuracy in text extraction.
Core Functionality
The script follows a systematic approach to data extraction:
1. Browser Automation
Using Playwright’s headless browser functionality, the script navigates to the target website, enters document numbers into the search field, and submits the form. This automation happens entirely in the background without requiring a visible browser window.
2. Data Extraction
Once search results are displayed, the script captures the entire page content through screenshots. It then uses OCR technology to extract text from these screenshots, providing a complete dataset for further processing.
3. PDF Processing
The script identifies and downloads any PDF links found on the results page. These PDFs often contain valuable supplementary information that might be needed for comprehensive data analysis.
4. Error Handling
Robust error handling mechanisms are implemented to manage timeouts, rate limiting, pagination issues, and processing exceptions, ensuring the script can handle the full volume of 20,000 document searches without interruption.
Advanced Data Structuring
To meet specific data formatting requirements, the developer plans to implement a template-based approach using AI. By leveraging LLM models like Gemini through prompt engineering, the script can extract and structure specific data points consistently across all document types.
Benefits of the Approach
This automated solution offers significant advantages:
- Time efficiency – processes thousands of documents without manual intervention
- Consistency – applies the same extraction logic across all documents
- Comprehensiveness – captures both visible webpage data and linked PDF content
- Adaptability – can be modified to extract different data points as needed
The finished script demonstrates the power of combining web automation, OCR technology, and AI-assisted data extraction to solve complex data collection challenges efficiently.