Optical Character Recognition (OCR)
Optical Character Recognition (OCR) is the technology that converts different types of documents, such as scanned paper documents, PDF files, or images, into editable and searchable data.
Overview
OCR technology enables the conversion of different types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data. It's a fundamental component of most IDP systems.
How OCR Works
- Pre-processing: Document images are cleaned up and prepared (deskewing, noise removal, binarization)
- Text Detection: Areas containing text are identified
- Character Recognition: Individual characters are recognized
- Post-processing: Results are refined using dictionaries and language models
Types of OCR
Traditional OCR
Traditional OCR uses pattern recognition to identify characters. It compares shapes against a stored library of character templates.
AI-powered OCR
Modern OCR systems use machine learning and neural networks, especially Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to improve recognition accuracy.
Key Considerations
Accuracy Factors
Several factors affect OCR accuracy:
- Image Quality: Resolution, contrast, noise
- Font Type and Size: Unusual fonts are harder to recognize
- Language: Some languages are more challenging than others
- Layout Complexity: Tables, columns, and mixed layouts are harder to process
Performance Metrics
Common metrics for evaluating OCR performance:
- Character Error Rate (CER): Percentage of incorrectly recognized characters
- Word Error Rate (WER): Percentage of incorrectly recognized words
- Processing Speed: Documents per minute or pages per second
Use Cases
- Document Digitization: Converting physical archives to digital format
- Form Processing: Extracting data from structured forms
- ID Verification: Reading information from ID cards and passports
- Mail Sorting: Automatically reading addresses on mail
- License Plate Recognition: Identifying vehicle license plates
OCR Technologies
| Technology | Developer | Strengths |
|---|---|---|
| Tesseract | Open-source, supports 100+ languages | |
| ABBYY FineReader | ABBYY | High accuracy, complex layout handling |
| Amazon Textract | Amazon | Cloud-based, integrates with AWS |
| Microsoft Azure OCR | Microsoft | Cloud-based, multilingual support |
Resources
- Understanding OCR Accuracy
- Tesseract OCR GitHub
- OCR Evaluation Tools
- Cem Dilmegani and Şevval Alper: OCR Benchmark
