OCRFlux (3B) - Local OCR AI Model Test | Turn PDFs into Markdown

OCRFlux (3B) is a 3B-parameter visual-language OCR fine-tune aimed at turning document images (including PDFs) into structured Markdown. In local tests on a Google Colab notebook with an Nvidia L4 GPU, it produced clean, well-formatted table HTML/Markdown and generally outperformed the previously used Nanonets OCRS model on the kinds of financial tables and receipts that tend to break OCR pipelines—though it still made recognizable text-level errors and raised licensing concerns for commercial use.

The evaluation starts with context from OCR-focused benchmarks where OCRFlux authors claim top performance against other OCR parsing models. The transcript notes a key caveat: comparisons mix models of different sizes (OCRFlux and Nanonets OCRS at 3B parameters, while at least one referenced competitor is larger), so benchmark superiority should be treated cautiously. Still, table extraction is highlighted as a particularly important benchmark dimension. OCRFlux appears strong on simpler tables, while another model (Monkey OCR) reportedly does better on complex tables.

For hands-on testing, the workflow loads the “chatoc ocr3b original model” (not a gguf/quantized variant, due to reported quantization issues). The extraction prompt is taken from the OCR toolkit repository to match the authors’ intended prompting. The system message is set to “you are a helpful assistant,” and generation uses temperature = 0; the tester reports that this setting improved results despite earlier warnings.

On an Nvidia financial report page (2026), OCRFlux returned structured JSON describing detected elements such as language rotation, tables, diagrams, and text. After post-processing into HTML/Markdown, the output looked “pretty much perfect” at first glance. A specific error was spotted: “Blackwell MVL72” was misread as “MVI727.2.” Even with that, table handling was a standout. Compared with Nanonets OCRS, OCRFlux avoided splitting header information across cells and preserved table structure more faithfully.

The model also correctly identified a page that was essentially a standalone table, producing table formatting that matched the underlying document upon manual checking. A receipt test further reinforced the advantage: OCRFlux extracted address, telephone number, order details, and item totals more reliably than Nanonets OCRS, which struggled with a particular table region. The receipt output was close but not flawless—some characters (like star symbols) were missing or altered.

Finally, an ID-card-like document (described as a fake ID card with account creation at Akma Corporation) showed the limits of the approach. The extraction became more disorganized, with no clear table structure, though the model still attempted to capture images/regions.

Overall, the local results lead to a practical recommendation: for PDF-to-Markdown pipelines (especially those emphasizing table extraction), OCRFlux (3B) is worth trying—potentially inside a DockLink-style pipeline. The transcript repeatedly flags that both OCRFlux and Nanonets OCRS are in preview and that a “Quen 2.5 VL license” may restrict commercial deployment, making licensing review essential before production use.