Falcon OCR Arabic: 270M Parameters State-of-the-Art Arabic OCR

Falcon-OCR-Arabic is a 270-million-parameter early-fusion OCR model from the Technology Innovation Institute adapted from Falcon OCR to read Arabic documents through supervised fine-tuning on real and synthetic data followed by reinforcement learning focused on transcription fidelity, diacritics, reading order, and table structure. Its shared Transformer architecture jointly processes image patches and generated text tokens, using prompts to select plain-text, LaTeX, or HTML table output without adding Arabic-specific modules. On a 11,974-sample benchmark spanning 15 Arabic document categories, it achieved 81.9% text accuracy, ranking second among 17 evaluated models behind Gemini 3.5 Flash at 84.3%, while producing the best table score with 59.95% Table TEDS. It outperformed larger general-purpose models and all dedicated OCR systems, including by more than 20 points over the strongest dedicated OCR comparator, and substantially improved over the English-oriented base model. The model performed especially well on official documents, administrative forms, receipts, and invoices, but trailed Gemini on visually dense newspapers, magazines, comics, and some handwriting tasks.

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