What is OCR (Optical Character Recognition)?
OCR (Optical Character Recognition) is technology that converts different types of documents - scanned paper documents, PDF files, or images - into editable and searchable text data. In RPA, OCR enables bots to 'read' documents that would otherwise be inaccessible, bridging the gap between paper-based and digital processes.
How OCR Works
Modern OCR uses a combination of techniques to extract text from images:
- Pre-processing: Clean up the image (deskew, noise removal, contrast enhancement)
- Text Detection: Identify areas containing text vs images or blank space
- Character Recognition: Match detected patterns to known characters
- Post-processing: Apply language models to correct errors and improve accuracy
Example Use Case
A logistics company receives shipping documents via email attachments (PDFs), fax (TIFF images), and scanned forms. OCR-powered automation extracts shipment details, addresses, and tracking numbers from all formats, automatically entering the data into the TMS system - reducing manual data entry by 90% and errors by 95%.
Key Benefits of OCR in Automation
Key Benefits
- Eliminate Manual Data Entry - Automatically extract data from any document
- Process Any Format - Handle PDFs, scans, photos, faxes, and more
- High Accuracy - Modern AI-OCR achieves 98%+ accuracy on quality documents
- Speed at Scale - Process thousands of pages per hour
- Searchable Archives - Make historical documents findable
- Reduce Errors - Consistent extraction without human fatigue
Types of OCR Technology
- Template-based OCR: Uses predefined zones for known document layouts
- Machine Learning OCR: Learns to recognize text patterns from training data
- ICR (Intelligent Character Recognition): Handles handwritten text
- IDP (Intelligent Document Processing): Combines OCR with AI for document understanding
Common OCR Use Cases in RPA
- Invoice Processing: Extract vendor, amounts, line items from any invoice format
- Form Processing: Digitize paper applications, surveys, and questionnaires
- ID Verification: Extract data from passports, driver's licenses, ID cards
- Contract Analysis: Identify key terms and clauses in legal documents
- Mail Processing: Sort and route incoming correspondence automatically
- Receipt Capture: Extract expense details for reimbursement processing
Improving OCR Accuracy
Tips for getting the best results from OCR:
- Document Quality: Higher resolution scans yield better results
- Pre-processing: Deskew, denoise, and enhance contrast
- Training Data: Train ML models on your specific document types
- Human-in-the-Loop: Review low-confidence extractions
- Validation Rules: Cross-check extracted data for consistency
BOTFORCE Discovery
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BOTFORCE Discovery helps you identify processes where OCR and document automation can eliminate manual data entry. Calculate the ROI of digitizing your paper-based workflows.
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