Optical Character Recognition
Optical Character Recognition (OCR) is the automated process of converting images of text—such as scanned documents or photos—into machine-encoded, editable, and searchable data.
Specialized Models & Emerging Trends
- Nanonets OCR Small: A newly introduced, highly efficient model featuring 3B parameters, specifically optimized for converting tables into text to support Retrieval-Augmented Generation (RAG) workflows.
- Shift Toward Efficiency: There is a growing industry trend toward smaller, specialized, and high-performance models, contrasting with larger-scale architectures such as Llama OCR and Mistral OCR.
- Infographic Text Correction: Utilizing Adobe Acrobat and Canva (‘Grab Text’) to identify and correct spelling inaccuracies or errors within text extracted from Gemini-generated infographics.
Backlinks:
- 2026 04 14 Nanonets OCR for tables to text for RAG
- 2026 04 27 Correcting AI Infographic Text Adobe Acrobat vs. Canva G
Source Notes
- 2026-04-14: “But OpenClaw is expensive…”
- 2026-04-07: Google Gemma 4 Open Weight Models Apache 20 and Enhanced AI · ▶ source
- 2026-04-08: LiteParse Free Local Layout Preserving Document Parsing for LLMs · ▶ source
- 2026-04-21: Google DeepMind
- 2026-04-22: Google Gemma · ▶ source