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
• Authors: Lixu Sun, Nurmemet Yolwas, Wushour Silamu,
• 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.

Related papers

• Entropy-Aware Structural Alignment for Zero-Shot Handwritten Chinese Character Recognition [7.632962062462334]
  Zero-shot Handwritten Chinese Character Recognition aims to recognize unseen characters by leveraging radical-based semantic compositions.<n>We propose an Entropy-Aware Structural Alignment Network that bridges the visual-semantic gap through information-theoretic modeling.<n>Our method establishes new state-of-the-art performance, achieving an accuracy of 55.04% on the ICDAR 2013 dataset.
  arXiv  Detail & Related papers  (2026-02-03T16:08:40Z)
• Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classification [0.0]
  This work introduces a cascading multi-agent framework that unifies complementary paradigms into a coherent and interpretable architecture.<n>Early modules perform reconstruction-gated filtering and object-level assessment, while higher-level reasoning agents are selectively invoked to interpret semantically ambiguous events.<n>The framework advances beyond conventional detection pipelines by combining early-exit efficiency, adaptive multi-agent reasoning, and explainable anomaly attribution, establishing a reproducible and energy-efficient foundation for scalable intelligent visual monitoring.
  arXiv  Detail & Related papers  (2026-01-08T11:31:47Z)
• VOIC: Visible-Occluded Decoupling for Monocular 3D Semantic Scene Completion [6.144392125326462]
  Camera-based 3D Semantic Scene Completion is a critical task for autonomous driving and robotic scene understanding.<n>Existing methods typically focus on end-to-end 2D-to-3D feature lifting and voxel completion.<n>We propose a novel dual-decoder framework that explicitly decouples SSC into visible-region semantic perception and occluded-region scene completion.
  arXiv  Detail & Related papers  (2025-12-22T02:05:45Z)
• Hybrid CNN-ViT Framework for Motion-Blurred Scene Text Restoration [2.0855516369698845]
  We introduce a hybrid deep learning framework that combines convolutional neural networks (CNNs) with vision transformers (ViTs)<n>The architecture employs a CNN-based encoder-decoder to preserve structural details, while a transformer module enhances global awareness through self-attention.<n>We show that the proposed method attains 32.20 dB in PSNR and 0.934 in SSIM, while remaining lightweight with 2.83 million parameters and an average inference time of 61 ms.
  arXiv  Detail & Related papers  (2025-11-08T17:48:58Z)
• LUMA: Low-Dimension Unified Motion Alignment with Dual-Path Anchoring for Text-to-Motion Diffusion Model [18.564067196226436]
  We propose a text-to-motion diffusion model that incorporates dual-path anchoring to enhance semantic alignment.<n>LUMA achieves state-of-the-art performance, with FID scores of 0.035 and 0.123, respectively.
  arXiv  Detail & Related papers  (2025-09-29T17:58:28Z)
• Mind-the-Glitch: Visual Correspondence for Detecting Inconsistencies in Subject-Driven Generation [120.23172120151821]
  We propose a novel approach for disentangling visual and semantic features from the backbones of pre-trained diffusion models.<n>We introduce an automated pipeline that constructs image pairs with annotated semantic and visual correspondences.<n>We propose a new metric, Visual Semantic Matching, that quantifies visual inconsistencies in subject-driven image generation.
  arXiv  Detail & Related papers  (2025-09-26T07:11:55Z)
• SPHERE: Semantic-PHysical Engaged REpresentation for 3D Semantic Scene Completion [52.959716866316604]
  Camera-based 3D Semantic Scene Completion (SSC) is a critical task in autonomous driving systems.<n>We propose the Semantic-PHysical Engaged REpresentation (SPHERE) for camera-based SSC.<n>SPHERE integrates voxel and Gaussian representations for joint exploitation of semantic and physical information.
  arXiv  Detail & Related papers  (2025-09-14T09:07:41Z)
• Generalized Decoupled Learning for Enhancing Open-Vocabulary Dense Perception [71.26728044621458]
  DeCLIP is a novel framework that enhances CLIP by decoupling the self-attention module to obtain content'' and context'' features respectively.<n>It consistently achieves state-of-the-art performance across a broad spectrum of tasks, including 2D detection and segmentation, 3D instance segmentation, video instance segmentation, and 6D object pose estimation.
  arXiv  Detail & Related papers  (2025-08-15T06:43:51Z)
• EgoSplat: Open-Vocabulary Egocentric Scene Understanding with Language Embedded 3D Gaussian Splatting [108.15136508964011]
  EgoSplat is a language-embedded 3D Gaussian Splatting framework for open-vocabulary egocentric scene understanding.<n>EgoSplat achieves state-of-the-art performance in both localization and segmentation tasks on two datasets.
  arXiv  Detail & Related papers  (2025-03-14T12:21:26Z)
• SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention [19.23636231942245]
  We propose a semantic-enhanced relational learning model based on a graph network with our designed memory graph attention layer. Our method replaces original language-independent encoding with cross-modal encoding in visual analysis. Experimental results on ReferIt3D and ScanRefer benchmarks show that the proposed method outperforms the existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-13T02:11:04Z)
• ELA: Efficient Local Attention for Deep Convolutional Neural Networks [15.976475674061287]
  This paper introduces an Efficient Local Attention (ELA) method that achieves substantial performance improvements with a simple structure. To overcome these challenges, we propose the incorporation of 1D convolution and Group Normalization feature enhancement techniques. ELA can be seamlessly integrated into deep CNN networks such as ResNet, MobileNet, and DeepLab.
  arXiv  Detail & Related papers  (2024-03-02T08:06:18Z)
• TC-Net: Triple Context Network for Automated Stroke Lesion Segmentation [0.5482532589225552]
  We propose a new network, Triple Context Network (TC-Net), with the capture of spatial contextual information as the core. Our network is evaluated on the open dataset ATLAS, achieving the highest score of 0.594, Hausdorff distance of 27.005 mm, and average symmetry surface distance of 7.137 mm.
  arXiv  Detail & Related papers  (2022-02-28T11:12:16Z)
• Spatio-Temporal Representation Factorization for Video-based Person Re-Identification [55.01276167336187]
  We propose Spatio-Temporal Representation Factorization module (STRF) for re-ID. STRF is a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID. We empirically show that STRF improves performance of various existing baseline architectures while demonstrating new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-07-25T19:29:37Z)
• Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Network [58.843211405385205]
  We propose a cross-order cross-semantic deep network (CCDN) to boost the semantic features learning for robust facial landmark detection. Specifically, a cross-order two-squeeze multi-excitation (CTM) module is proposed to introduce the cross-order channel correlations for more discriminative representations learning. A novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection.
  arXiv  Detail & Related papers  (2020-11-16T08:19:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.