📅 2024-12-22 — Session: OCR Data Processing and Enhancement
🕒 21:50–22:00
🏷️ Labels: OCR, Data Cleaning, PDF, Data Extraction, Automation
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to enhance the Optical Character Recognition (OCR) process for better data extraction and cleaning, specifically focusing on transaction data from PDF documents.
Key Activities
- Data Cleaning and Structuring: The OCR output data was cleaned and structured into a CSV format, and a DataFrame was created for better organization and usability.
- Refinement of OCR Process: Identified issues with OCR output quality and proposed adjustments to enhance text extraction for meaningful transaction data.
- Improvement of OCR Data Extraction: Addressed challenges of noise and formatting issues in refined OCR data and applied advanced text-cleaning techniques for improved accuracy.
- Enhancement of OCR Results: Strategies were developed to enhance OCR results, including improving image quality, manual parsing support, and utilizing specialized OCR tools.
- Transaction Details Extraction: Processed PDF content to extract relevant transaction details and created a structured dataset.
- Successful Extraction and Processing: Transactions were successfully extracted from PDFs into a structured table, and micro-transactions were consolidated into a single DataFrame.
Achievements
- Successfully enhanced the OCR process for better data extraction and cleaning.
- Created structured datasets from OCR and PDF data, ready for review or export.
Pending Tasks
- Further adjustments or analyses on the extracted data can be requested if needed.