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Found 11 papers, 7 papers with code
HierCode: A Lightweight Hierarchical Codebook for Zero-shot Chinese Text Recognition
Text recognition, especially for complex scripts like Chinese, faces unique challenges due to its intricate character structures and vast vocabulary.
Conditional Text Image Generation with Diffusion Models
Current text recognition systems, including those for handwritten scripts and scene text, have relied heavily on image synthesis and augmentation, since it is difficult to realize real-world complexity and diversity through collecting and annotating enough real text images.
Denoising Diffusion Models for Plug-and-Play Image Restoration
Although diffusion models have shown impressive performance for high-quality image synthesis, their potential to serve as a generative denoiser prior to the plug-and-play IR methods remains to be further explored.
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
To leverage strong generative priors and address challenges such as unstable training and lack of interpretability for GAN-based generative methods, we propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM).
SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text
Specifically, we propose a style bank to parameterize the specific handwriting styles as latent vectors, which are input to a generator as style priors to achieve the corresponding handwritten styles.
Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter
Specifically, we integrate IFA into the two most prevailing text recognition streams (attention-based and CTC-based) and propose attention-guided dense prediction (ADP) and Extended CTC (ExCTC).
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Text Recognition in the Wild: A Survey
This paper aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition; (2) introduce new insights and ideas; (3) provide a comprehensive review of publicly available resources; (4) point out directions for future work.
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
An agent network learns from the output of the recognition network and controls the fiducial points to generate more proper training samples for the recognition network.
Decoupled Attention Network for Text Recognition
To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results.
Ranked #4 on
Scene Text Recognition
on ICDAR 2003
Adaptive Embedding Gate for Attention-Based Scene Text Recognition
Scene text recognition has attracted particular research interest because it is a very challenging problem and has various applications.
Aggregation Cross-Entropy for Sequence Recognition
In this paper, we propose a novel method, aggregation cross-entropy (ACE), for sequence recognition from a brand new perspective.
