Open-Source Models Advance OCR Workflows

As AI systems move beyond text, the capabilities of Optical Character Recognition (OCR) are expanding significantly, driven by the emergence of powerful Vision-Language Models (VLMs). These advancements are redefining how documents are processed, moving beyond simple text extraction to encompass complex visual and semantic understanding. The increasing availability of open-source models offers advantages in terms of cost-efficiency and privacy, making sophisticated OCR solutions more accessible.

Key Points

Modern OCR models, often fine-tuned from existing VLMs, now offer capabilities far exceeding traditional text recognition. These include the ability to process low-quality scans, understand complex document elements like tables and charts, and integrate visual and textual content for tasks such as document retrieval and question answering.

Several key factors guide the selection of an appropriate OCR model:

• Model Capabilities: Beyond text recognition, models vary in their ability to handle complex components (images, charts, tables) and their support for different output formats (DocTag, HTML, Markdown, JSON).

• Positional Awareness: Modern models embed layout information, such as text bounding boxes, to maintain reading order and semantic coherence, reducing "hallucination."

• Prompting: Some models support prompt-based task switching, allowing for dynamic task definition, while others operate with fixed system prompts.

• Cost and Efficiency: Model size, inference frameworks (e.g., vLLM, SGLang), and the availability of quantized versions influence operational costs, with open-source models generally offering more economical large-scale deployment.

• Evaluation Benchmarking: No single model is universally optimal. Performance varies across document types, languages, and tasks, necessitating the use of benchmarks like OmniDocBenchmark, OlmOCR-Bench, and CC-OCR, or custom test sets for specific business domains.

Cutting-Edge Open-Source OCR Models

The open-source ecosystem has fostered rapid innovation in OCR. Notable models include:

• Chandra: A 9B parameter model with grounding capabilities, supporting over 40 languages, and achieving an average score of 83.1 ± 0.9 on the OlmOCR Benchmark.

• OlmOCR-2: An 8B parameter model optimized for batch processing, with grounding capabilities, and a score of 82.3 ± 1.1 on the OlmOCR Benchmark.

• Nanonets-OCR2-3B: A 4B parameter model offering structured Markdown output, automatic image captioning, and support for multiple languages.

• PaddleOCR-VL: A smaller 0.9B parameter model supporting 109 languages, capable of converting tables and charts to HTML.

• DeepSeek-OCR: A 3B parameter model known for general visual understanding, efficient memory usage, and strong image text recognition across nearly 100 languages.

• Granite-Docling-258M: A 258M parameter model that uses DocTags and supports prompt-based task switching.

• Qwen3-VL: A powerful general-purpose multimodal language model that, while not specifically optimized for OCR, can perform various document understanding tasks.

These models often feature layout-aware capabilities and can parse tables, charts, and mathematical formulas.

Open-Source OCR Datasets

Despite the proliferation of open-source models, publicly available training and evaluation datasets remain relatively scarce. AllenAI's olmOCR-mix-0225 is a notable exception, having been used to train numerous models. Broader data sharing, including synthetic data generation, VLM automatic transcription, and the systematic organization of existing corpora, is expected to further advance open-source OCR.

Model Running Tools

For developers, several methods facilitate running OCR models:

• Local Execution: Many advanced OCR models support vLLM and can be loaded via the transformers library for inference. This allows for direct local processing.

• MLX (for Apple Silicon): Apple's machine learning framework, MLX, enables efficient execution of visual language models on Apple Silicon devices.

• Remote Execution: Services like Hugging Face Inference Endpoints provide managed hosting for vLLM or SGLang compatible models, offering GPU acceleration, auto-scaling, and monitoring. For batch processing, Hugging Face Jobs, with specialized scripts like uv-scripts/ocr, can efficiently handle large volumes of images without requiring local GPU resources.

Beyond OCR

The field of Document AI extends beyond basic text recognition:

• Visual Document Retrieval: This involves retrieving relevant PDF documents based on text queries, directly searching at the "document image" level. These models can be integrated into multimodal Retrieval-Augmented Generation (RAG) pipelines.

• Document Question Answering: Instead of converting documents to plain text and then feeding them to a Large Language Model (LLM), a more effective approach involves directly inputting the original document image and user question into a multimodal VLM, such as Qwen3-VL. This preserves visual and contextual information that might be lost in text-only conversions.

From a structural standpoint, open-source visual language models are transforming OCR, offering unprecedented freedom and innovation for developers and researchers in document intelligence.