DeepSeek-OCR-2 is a vision-language OCR model developed by DeepSeek, designed for high-quality document parsing, optical character recognition, and structured information extraction from complex visual documents. This repository contains GGUF quantized variants of the model optimized for efficient local inference using llama.cpp.
The model is built to accurately recognize and interpret textual information from a wide variety of document types, including scanned pages, invoices, receipts, forms, tables, books, reports, and handwritten or digitally generated content. Beyond text recognition, it emphasizes document parsing and contextual understanding to enable reliable extraction of structured information from visually complex documents.
The quantized formats significantly reduce memory requirements while maintaining strong OCR accuracy and document comprehension, making the model suitable for both local deployment and enterprise-scale document-processing pipelines.
This repository provides various GGUF quantized versions of the DeepSeek-OCR-2 model optimized for efficient local inference using llama.cpp.
DeepSeek-OCR-2 is trained with a strong emphasis on document parsing, optical character recognition, and multimodal document understanding across diverse visual document collections.
Optical Character Recognition (OCR) : Accurately extracts textual content from scanned documents, images, and visual media.
Document Parsing : Understands document layout and semantic organization for structured processing.
Layout Understanding : Identifies relationships between document elements including paragraphs, tables, and forms.
Structured Information Extraction : Extracts key-value information and structured content from complex documents.
Complex Document Analysis : Handles visually rich documents containing mixed layouts, tables, and embedded content.
Efficient Local Deployment : Quantized variants enable practical OCR inference on consumer hardware.
./llama-mtmd-cli \
-m SandlogicTechnologies/DeepSeek-OCR-2_IQ4_NL.gguf \
--mmproj SandlogicTechnologies/deepseek-ocr-2.mmproj-f16.gguf \
--image invoice.png \
-p "Extract all document fields and return them as structured JSON."
Document Digitization : Convert scanned documents into searchable digital content.
Enterprise OCR : Automate extraction of structured information from business documents.
Invoice & Receipt Processing : Capture financial information from invoices and receipts.
Document Parsing Pipelines : Build automated workflows for layout-aware document understanding.
Knowledge Extraction : Prepare structured document data for indexing, search, and downstream AI systems.
Research & Evaluation : Benchmark OCR and document-understanding performance across diverse datasets.
These quantized models are based on the original work by the DeepSeek AI development team.
Special thanks to:
llama.cpp open-source community for enabling efficient quantization and inference via the GGUF format.For questions, feedback, or support, please reach out at support@sandlogic.com or visit https://www.sandlogic.com/