One Glyph, Many Tokens: Tokenization Bottlenecks in Generative OCR

In generative optical character recognition (OCR), a visually atomic character may correspond to multiple output tokens. This representation mismatch forces the model to transcribe a single glyph through a sequence of autoregressive predictions, increasing the risk of error propagation. Our analysis reveals a pronounced degradation in OCR performance as token fragmentation increases, suggesting that output representation itself can remain a bottleneck. To address this, we introduce Glyph-Aligned Output-Space Adaptation (GAOSA), a representation level adaptation approach that aligns the output granularity of fragmented characters with visually atomic glyphs. GAOSA replaces fragmented multi-token decoding paths with atomic character-level output units. It initializes these units from their constituent-token representations and jointly adapts the token embeddings and language-modeling head under transcription supervision. We primarily study GAOSA in historical Chinese OCR, where standard SFT improves target character F1 from 7.6% to 59.7%, while GAOSA further raises it to 88.6%, demonstrating substantial gains beyond those achieved through supervision alone. To support full-page transcription, we further synthesize target characters into historical layouts and train with page-level supervision, enabling the model to jointly learn layout understanding and character recognition. The resulting model achieves a normalized edit distance (NED) of 0.11 on AncientDoc, compared with 0.19 for PaddleOCR-VL-1.6 and 0.18 for Qwen3.6-27B. In addition, GAOSA yields consistent gains across distinct scripts and symbol systems. On HHD-Ethiopic, GAOSA improves F1 from 42.6% (SFT only) to 76.2%; on OCRIPA, it improves F1 from 73.9% to 90.8%. These results establish glyph-aligned output representation as an effective intervention beyond conventional SFT across diverse scripts and symbol systems. The model and benchmarks will be publicly available.