Optical Character Recognition

AI-powered OCR automates text extraction from images and documents for faster processing.

Business impact

• Operational Efficiency — Speeds up document processing by automating text extraction from images and scans
• Data Accuracy — Reduces human errors in manual data entry through AI-based recognition
• Processing Speed — Accelerates conversion of physical documents into searchable digital text
• Cost Reduction — Lowers labor and operational costs by minimizing manual transcription tasks
• Customer Satisfaction — Improves user experience with faster and more reliable document handling
• Compliance Accuracy — Enhances regulatory adherence by accurately extracting contract and compliance data
• Error Rate — Decreases errors in data capture from documents and images
• Fraud Detection Rate — Supports anomaly detection by analyzing text and markings on physical items

Data requirements

• Scanned Documents (Image) — Source images containing text for OCR processing
• Photographs from Cameras (Image) — Capture real-world text such as license plates or receipts
• PDF Files (Structured) — Provide digital documents for text extraction
• Video Frames (Video) — Extract text appearing in video content for analysis
• Text Annotations (Text) — Used for training and validating OCR models

AI methods and techniques

• Predictive AI — Predicts character sequences from image features to convert text accurately
• Generative AI — Generates corrected or enhanced text outputs from noisy or incomplete inputs
• Symbolic AI — Applies rule-based post-processing to validate and structure recognized text

AI models and model families

GPT-4o, Claude, LLaMA, TrOCR, InternVL, Qwen, MiniCPM

Industries

Real-world evidence

9 documented case studies on record.

Companies using this: A MCO US Health Insurance Marketplace, Bigme, Clockworks, Cybord, DFRobot, Google, Kaspersky, NPR, Sprinklr.