OTSNet: A Neurocognitive-Inspired Observation-Thinking-Spelling Pipeline for Scene Text Recognition

• URL: http://arxiv.org/abs/2511.08133v1
• Date: Wed, 12 Nov 2025 01:41:50 GMT
• Title: OTSNet: A Neurocognitive-Inspired Observation-Thinking-Spelling Pipeline for Scene Text Recognition
• Abstract summary: Scene Text Recognition (STR) remains challenging due to real-world complexities.<n>We propose OTSNet, a novel three-stage network embodying a neurocognitive-inspired Observation-Thinkingpelling pipeline for unified STR modeling.<n>OTSNet achieves 83.5% average accuracy on the Union14M-L benchmark and 79.1% on the heavily occluded OST-establishing new records across 9 out of 14 evaluation scenarios.
• Score: 3.5518986305758027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene Text Recognition (STR) remains challenging due to real-world complexities, where decoupled visual-linguistic optimization in existing frameworks amplifies error propagation through cross-modal misalignment. Visual encoders exhibit attention bias toward background distractors, while decoders suffer from spatial misalignment when parsing geometrically deformed text-collectively degrading recognition accuracy for irregular patterns. Inspired by the hierarchical cognitive processes in human visual perception, we propose OTSNet, a novel three-stage network embodying a neurocognitive-inspired Observation-Thinking-Spelling pipeline for unified STR modeling. The architecture comprises three core components: (1) a Dual Attention Macaron Encoder (DAME) that refines visual features through differential attention maps to suppress irrelevant regions and enhance discriminative focus; (2) a Position-Aware Module (PAM) and Semantic Quantizer (SQ) that jointly integrate spatial context with glyph-level semantic abstraction via adaptive sampling; and (3) a Multi-Modal Collaborative Verifier (MMCV) that enforces self-correction through cross-modal fusion of visual, semantic, and character-level features. Extensive experiments demonstrate that OTSNet achieves state-of-the-art performance, attaining 83.5% average accuracy on the challenging Union14M-L benchmark and 79.1% on the heavily occluded OST dataset-establishing new records across 9 out of 14 evaluation scenarios.

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