📅 2024-12-22 — Session: Enhanced PDF and Transaction Parsing
🕒 23:15–23:55
🏷️ Labels: Pdf Parsing, Regex, Data Extraction, OCR, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to improve the parsing logic for financial statements and transaction data, specifically focusing on extracting and processing text from PDFs and refining regex patterns for transaction parsing.
Key Activities
- Developed strategies for parsing financial statements, focusing on regex implementation for both regular transactions and installment payments.
- Addressed issues with PDF transaction parsing, including debugging regex misalignment and refining patterns.
- Tackled challenges in text extraction from PDFs, using OCR as a fallback for image-based PDFs and refining scripts for better extraction.
- Updated parsing logic to successfully extract transactions, handling regular and installment data effectively.
- Adjusted scripts to bypass OCR when possible and directly extract text from PDFs.
- Improved regex patterns for transaction parsing, ensuring correct matching and data capture.
- Resolved directory path errors in scripts and converted Spanish month abbreviations to datetime format in Pandas.
Achievements
- Successfully implemented OCR for text extraction from PDFs, improving data capture.
- Enhanced regex patterns to accurately parse transaction lines, including date validation.
- Improved script functionality for PDF text extraction, maintaining layout and line breaks.
Pending Tasks
- Further refine regex patterns for edge cases in transaction parsing.
- Continue testing and debugging PDF text extraction scripts for various PDF formats.