Task-Adaptive Reinforcement Learning for Unified OCR in Maritime and Complex Documents

Abstract

Maritime logistics relies on documents that combine dense multilingual text, irregular layouts, and task-specific output schemas, requiring operational systems to recover text, structure, location, and semantics. We trained Hi-Dolphin-OCRusing a task-adaptive reinforcement-learning framework that formulates text spotting, structured localization, document parsing, information extraction, and visual question answering as a unified instruction-following problem. The framework combines task-routed verifiable rewards with bidirectional spatial coverage for spotting, a hard degeneration gate for repetitive, symbol-dominated, and overlong responses, and difficulty-aware sampling based on the supervised model’s error distribution. To our knowledge, we also release the first publicly available maritime-oriented multi-task OCR dataset, Maritime-OCR-Bench, comprising 1888 maritime and complex-document examples across five evaluation tasks. On this benchmark, the failure-aware aggregate score increases from 0.7589 for the supervised checkpoint to 0.7900 after reinforcement learning, with the largest differences observed for coordinate-bearing structured outputs. These results support task-aligned reinforcement learning as a practical strategy for unified OCR, while the released dataset provides a reproducible basis for evaluating spatial, structural, and semantic document understanding in maritime-oriented settings.