Using this approach, we built Nemotron OCR v2, a multilingual OCR model that is both accurate and fast. Accuracy is driven by data: 12 million synthetic training images across six languages brought NED scores from 0.56–0.92 down to 0.035–0.069 on non-English languages. Speed is driven by architecture: a shared detection backbone whose features are reused by both the recognizer and relational model, eliminating redundant computation and enabling 34.7 pages/second on a single A100 GPU. The synthetic data pipeline is generic enough to extend to any language for which fonts and source text exist.

The dataset is publicly available at nvidia/OCR-Synthetic-Multilingual-v1 and the model at nvidia/nemotron-ocr-v2. You can try the model directly in the browser at the Nemotron OCR v2 demo.

Nemotron OCR v1 was a strong English OCR model, but it was not trained for multilingual purposes so when exposed to other languages it failed to read the documents accurately. On our SynthDoG benchmark, v1 produced Normalized Edit Distance (NED) scores between 0.56 and 0.92 for Japanese, Korean, Russian, and Chinese. At these error rates, the model output bears little resemblance to the ground truth.

Part of the issue was the character set. The v1 model supported only 855 characters, which simply did not cover CJK (Chinese, Japanese, Korean) or Cyrillic scripts. We ran an experiment where we expanded the character set to 14,244 characters to cover all the target languages. This helped slightly, but without sufficient training data actually containing those characters, the improvement was marginal. The model could theoretically output the right characters, but it had never learned what they looked like. The bottleneck was data, not architecture.

Collecting and annotating millions of real-world images across six languages with word-, line-, and paragraph-level bounding boxes plus reading order graphs would be prohibitively expensive. We needed a different approach.