Papers Scene Text Recognition
“Scene Text Recognition” 태그가 달린 논문 282편 · 필터 해제
All-in-One Multilingual Scene Text Recognition with Script-aware Mixture-of-Experts
Multilingual scene text recognition (STR) remains challenging due to the scarcity of training data for most languages and the difficulty of serving diverse scripts within a single model. Existing solutions either deploy …
Scene Text RecognitionOut-of-Length Scene Text Recognition: A Two-Axis Diagnosis and a Training-Free Fix
Scene Text Recognition (STR) models are trained almost exclusively on word crops of at most 25 characters, yet real deployments (signage, product labels, dense captions) require reading much longer text. This paper diagn…
Scene Text RecognitionAdvancing WordArt-Oriented Scene Text Recognition: Datasets and Methods
WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially more challenging than general Scene Text Recognition (STR). Existing S…
Scene Text RecognitionReading in the Dark: Low-light Scene Text Recognition
Accurate text recognition in low-light environments is essential for intelligent systems in applications ranging from autonomous vehicles to smart surveillance. However, challenges such as poor illumination and noise int…
Low-Light Image EnhancementScene Text RecognitionAutonomous VehiclesMulti-Modal Character Localization and Extraction for Chinese Text Recognition
Scene text recognition (STR) methods have demonstrated their excellent capability in English text images. However, due to the complex inner structures of Chinese and the extensive character categories, it poses challenge…
Scene Text RecognitionAn Effective Data Augmentation Method by Asking Questions about Scene Text Images
Scene text recognition (STR) and handwritten text recognition (HTR) face significant challenges in accurately transcribing textual content from images into machine-readable formats. Conventional OCR models often predict …
Handwritten Text RecognitionScene Text RecognitionData AugmentationWhat Is Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution
Large-scale and categorical-balanced text data is essential for training effective Scene Text Recognition (STR) models, which is hard to achieve when collecting real data. Synthetic data offers a cost-effective and perfe…
Scene Text RecognitionMDiff4STR: Mask Diffusion Model for Scene Text Recognition
Mask Diffusion Models (MDMs) have recently emerged as a promising alternative to auto-regressive models (ARMs) for vision-language tasks, owing to their flexible balance of efficiency and accuracy. In this paper, for the…
Scene Text RecognitionBharat Scene Text: A Novel Comprehensive Dataset and Benchmark for Indian Language Scene Text Understanding
Reading scene text, that is, text appearing in images, has numerous application areas, including assistive technology, search, and e-commerce. Although scene text recognition in English has advanced significantly and is …
Scene Text RecognitionScene Text DetectionOTSNet: A Neurocognitive-Inspired Observation-Thinking-Spelling Pipeline for Scene Text Recognition
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 …
Scene Text RecognitionLadderMoE: Ladder-Side Mixture of Experts Adapters for Bronze Inscription Recognition
Bronze inscriptions (BI), engraved on ritual vessels, constitute a crucial stage of early Chinese writing and provide indispensable evidence for archaeological and historical studies. However, automatic BI recognition re…
Scene Text RecognitionTEACH: Text Encoding as Curriculum Hints for Scene Text Recognition
Scene Text Recognition (STR) remains a challenging task due to complex visual appearances and limited semantic priors. We propose TEACH, a novel training paradigm that injects ground-truth text into the model as auxiliar…
Scene Text RecognitionESTR-CoT: Towards Explainable and Accurate Event Stream based Scene Text Recognition with Chain-of-Thought Reasoning
Event stream based scene text recognition is a newly arising research topic in recent years which performs better than the widely used RGB cameras in extremely challenging scenarios, especially the low illumination, fast…
Scene Text RecognitionLogical ReasoningLinguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition
Text images are unique in their dual nature, encompassing both visual and linguistic information. The visual component encompasses structural and appearance-based features, while the linguistic dimension incorporates con…
Contrastive LearningScene Text RecognitionSelf-Supervised Learningself-supervised scene text recognitionEfficient and Accurate Scene Text Recognition with Cascaded-Transformers
In recent years, vision transformers with text decoder have demonstrated remarkable performance on Scene Text Recognition (STR) due to their ability to capture long-range dependencies and contextual relationships with hi…
DecoderScene Text RecognitionAccurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation
Scaling architectures have been proven effective for improving Scene Text Recognition (STR), but the individual contribution of vision encoder and text decoder scaling remain under-explored. In this work, we present an i…
DecoderScene Text RecognitionA Context-Driven Training-Free Network for Lightweight Scene Text Segmentation and Recognition
Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive for real-time scenarios. In such cases, the deployment of heavy models …
Scene Text RecognitionText DetectionText SegmentationEventSTR: A Benchmark Dataset and Baselines for Event Stream based Scene Text Recognition
Mainstream Scene Text Recognition (STR) algorithms are developed based on RGB cameras which are sensitive to challenging factors such as low illumination, motion blur, and cluttered backgrounds. In this paper, we propose…
Large Language ModelScene Text RecognitionBillet Number Recognition Based on Test-Time Adaptation
During the steel billet production process, it is essential to recognize machine-printed or manually written billet numbers on moving billets in real-time. To address the issue of low recognition accuracy for existing sc…
Scene Text RecognitionTest-time AdaptationText DetectionOcean-OCR: Towards General OCR Application via a Vision-Language Model
Multimodal large language models (MLLMs) have shown impressive capabilities across various domains, excelling in processing and understanding information from multiple modalities. Despite the rapid progress made previous…
document understandingLanguage ModelingLanguage ModellingOptical Character Recognition (OCR)+1