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Optical character recognition - Wikipedia
Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a…
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Optické rozpoznávání znaků – Wikipedie
OCR – zpracování textu z tištěné do elektronické podoby je použitelné pro všechny tištěné výstupy z laserových, inkoustových, termosublimačních a jehličkových tiskáren a samozřejmě pro předlohy vytištěné knihtiskem.
Best OCR software of 2025 | TechRadar
We tested out the best OCR software for scanning your paper documents and archiving them as digital PDF files.
What is Optical Character Recognition (OCR)?
OCR is a technology that converts images of text into machine-readable text format. This converted text can be edited, copied, and shared as if it were typed out. An OCR scanner first scans the image to identify patterns, forming text and numbers. It then compares these patterns with a database of text in various languages to effectively convert the image into readable text.Which is the best OCR software?
Adobe Scan is the best OCR scanner out there. It can convert any scanned image or document into searchable and readable text. You can even convert a file from one format to the other and access a lot of PDF templates and edit tools with Adobe.It also comes with an AI assistant that can help you analyze your documents and generate new ideas and content at an additional cost of just $4.99/month. Although there's a free Adobe Scan plan, it does not come with the OCR scanner. For that, you'll need to switch to the paid plans, which start from $12.99/month.
Is there a free OCR scanner?
Microsoft Lens is arguably the best free OCR scanner. As we expect from free software, Lens is extremely easy to use. Simply scan any document or whiteboard using the mobile app with the “text” mode activated. Lens will automatically extract text from the image, which can be copied and shared. However, for more advanced features such as real-time editing, we recommend opting for a paid software like Adobe Scan.Top OCR Softwares [Compared & Reviewed] in 2025 | Nanonets
Looking for the right OCR software? Read this in-depth buyer's guide to find the perfect one for your business.
What is the best OCR software for extracting text from scanned documents in 2025?
The "best" OCR (Optical Character Recognition) software for extracting text from scanned documents in 2025 is typically an AI-powered Intelligent Document Processing (IDP) platform, which goes beyond basic text recognition to understand document context and structure.Top contenders include:
- Nanonets: A leading IDP platform renowned for its AI-powered OCR. It excels at extracting data from any scanned document (even low quality or handwritten) by understanding context and layouts, not just characters. Its adaptive learning improves accuracy over time.
- ABBYY FineReader / Vantage: Established enterprise solutions known for high accuracy on various document types, including complex layouts and handwriting.
- Kofax (Tungsten Automation): Offers robust AI-powered OCR engines, emphasizing batch processing and tailored for specific industries like finance and legal.
- Cloud AI Services: Google Document AI, Amazon Textract, and Microsoft Azure AI Document Intelligence provide highly accurate, scalable OCR services, leveraging advanced machine learning for text, forms, and tables from scans.
- UiPath Document Understanding / Automation Anywhere Document Automation: Integrate powerful OCR and AI into broader RPA platforms.
- Tesseract OCR: An open-source engine (by Google), highly customizable for developers, supporting over 100 languages. It forms the base for many custom OCR solutions but requires significant development for advanced IDP.
The best choice depends on document complexity, volume, required accuracy, budget, and integration needs. For extracting structured data from diverse, complex scanned documents, AI-driven IDP solutions like Nanonets often lead in accuracy and ease of implementation.
How to compare different OCR tools for invoice processing?
Comparing OCR tools for invoice processing requires evaluating specific criteria beyond basic text extraction, focusing on features crucial for Accounts Payable (AP) automation.Key comparison points:
- Data Extraction Accuracy (Crucial):
- Header vs. Line Items: Can it accurately extract not just header details (vendor, date, total) but also complex, multi-line item details (SKUs, quantities, unit prices, discounts, taxes)? This is a major differentiator. Nanonets excels in granular line-item extraction.
- Layout Agnosticism: Does it handle invoices from any vendor without needing manual templates for each new layout? Or does it break when layouts vary? AI-powered tools like Nanonets are layout-agnostic.
- Input Quality: How well does it perform on poor scan quality, crumpled receipts, or handwritten notes on invoices?
- AI & Machine Learning Capabilities:
- Contextual Understanding: Does the AI understand the meaning of data (e.g., "PO Number" vs. "Invoice Number")?
- Adaptive Learning: Does the system learn from human corrections, improving accuracy over time for your specific invoices? (Nanonets features this).
- Automated GL Coding: Can it intelligently suggest or apply GL codes based on past patterns?
- Validation & Exception Handling:
- Automated Validation Rules: Can you set up rules to validate extracted data (e.g., matching totals, valid dates, PO cross-referencing)?
- Human-in-the-Loop (HITL): How user-friendly is the interface for human review and correction of flagged exceptions?
- Integration Capabilities:
- ERP/Accounting Software: Does it offer direct, pre-built connectors for your ERP (e.g., SAP, NetSuite) or accounting software (QuickBooks, Xero)?
- API/Webhooks: Robust API for custom integrations.
- Workflow Automation: Does it just extract, or can it also manage approval workflows, 2-way/3-way matching, and automated posting to your AP system?
- Scalability & Performance: Can it handle your current and future invoice volumes efficiently?
- Pricing Model: Per-invoice, per-page, or subscription tiers? Consider Total Cost of Ownership (TCO).
Conducting a pilot test with your own diverse invoice samples on selected tools (like Nanonets) is the most effective way to compare real-world performance.
How does OCR accuracy vary across different document types like ID cards and receipts?
OCR accuracy varies significantly across document types like ID cards and receipts due to fundamental differences in their structure, data density, print quality, and usage conditions. OCR tools, especially AI-powered ones, are often specialized to achieve high accuracy for particular document types.- ID Cards (Driver's Licenses, Passports, National IDs):
- Characteristics: Highly standardized formats (within a country/state), often contain machine-readable zones (MRZ), specific fonts, security features (holograms, micro-text), and sensitive PII. Usually rigid layouts.
- Accuracy: Very high. Advanced OCR/IDP solutions (like Nanonets' ID Card OCR or Driver License OCR) can achieve 95-99%+ field extraction accuracy for visible fields on good quality images. This is because they are trained on vast datasets of specific ID types and leverage Computer Vision for precise field location. Challenges arise with glare, poor photos, or severe damage.
- Receipts:
- Characteristics: Highly unstructured and variable. Merchants have unique layouts, fonts, and sizes. Often poor quality (crumpled, faded thermal print, blurry photos, dense text). May contain handwritten tips.
- Accuracy: More challenging. Basic OCR struggles immensely, yielding low accuracy. AI-powered Receipt OCR (e.g., Nanonets') uses advanced image pre-processing and context-aware AI to achieve 85-98% accuracy. Accuracy depends on how well the AI handles specific merchant formats, print quality, and handwritten elements. Multi-line item extraction is also more complex.
General Factors Affecting Both:
- Image Quality: Higher resolution (300 DPI+), good lighting, no blur/skew, and contrast significantly boost accuracy for both.
- AI Sophistication: AI/ML models trained on vast, diverse datasets specific to the document type perform far better than generic OCR.
- Human-in-the-Loop (HITL): A crucial human review step for low-confidence extractions allows achieving 100% data accuracy for critical fields, while also improving the AI model through adaptive learning.
In summary, while ID cards benefit from high standardization and dedicated training, receipts demand more sophisticated AI (like Nanonets' specialized receipt models) due to their inherent variability and often poor physical condition.
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