TessMark Features You Should Be Using Today
1. Intelligent OCR
- Converts scanned documents and images into editable text with high accuracy.
- Supports multi-language recognition and handwriting detection for mixed-content files.
2. Batch Processing
- Process large sets of documents simultaneously to save time.
- Automated job queuing with retry and error reporting for failed files.
3. Document Structure Detection
- Identifies headings, tables, lists, and paragraphs to preserve original layout.
- Exports structured output (JSON, XML) for downstream parsing and data extraction.
4. Named Entity Recognition (NER)
- Automatically tags names, dates, locations, organizations, and custom entities.
- Allows rule-based and ML-fine-tuned entity sets for domain-specific extraction.
5. Annotation & Review Tools
- In-app highlights, comments, and correction workflow for human reviewers.
- Version history and approval tracking to maintain audit trails.
6. Integration APIs
- RESTful APIs and SDKs for common languages (Python, JavaScript) to embed TessMark into pipelines.
- Webhooks and SSO support for enterprise authentication and automation.
7. Privacy & On-Premise Options
- Configurable deployment: cloud, private cloud, or on-premise to meet compliance needs.
- Data encryption at rest and in transit with fine-grained access controls.
8. Export & Format Support
- Save outputs as searchable PDFs, DOCX, CSV, JSON, or export directly to databases.
- Preserve original image quality and layout options for archival purposes.
9. Custom Model Training
- Fine-tune OCR and NER models using your labeled datasets for improved accuracy.
- Model versioning and A/B testing to measure improvements.
10. Analytics & Monitoring
- Usage dashboards showing throughput, accuracy metrics, and error trends.
- Alerts for pipeline failures, latency spikes, and quota limits.
If you want, I can:
- Provide a short how-to for enabling any specific feature (pick one), or
- Draft example API calls for integrating TessMark into a Python pipeline.
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