Building a Fast Multilingual OCR Model with Synthetic Data

Back to Articles Building a Fast Multilingual OCR Model with Synthetic Data Enterprise + Article Published April 17, 2026 Upvote 4 Ryan Chesler emelryan Follow nvidia Training a high-quality OCR model requires a large quantity of annotated image-text pairs: images with precise bounding boxes, transcriptions, and ideally reading order information at the word, line, and paragraph level. Every approach to curating this data comes with tradeoffs. Existing benchmark datasets like ICDAR and Total-Text have clean labels but limited scale, typically tens of thousands of images skewed toward English and Chinese. Manual annotation produces the highest quality labels but is expensive and slow, making it impractical at the millions-of-images scale needed for robust multilingual models. Web-scraped PDFs offer enormous quantity, but the embedded text is often noisy: characters recorded as individual strokes instead of words, text baked into images with no extractable layer, or scanned pages where a weak OCR model was applied and the resulting text layer is unreliable. You can extract usable signal from web PDFs, but it takes significant filtering effort and the result is never perfectly clean. Synthetic data generation offers a way out of these tradeoffs. By rendering text onto images programmatically, we get both the scale of web scraping and the label purity of hand annotation. Every bounding box, transcription, and reading order relationship is known exactly because we placed it ther...