Scene Text Recognition with Permuted Autoregressive Sequence Models [PDF]
European Conference on Computer Vision, 2022 Context-aware STR methods typically use internal autoregressive (AR) language models (LM). Inherent limitations of AR models motivated two-stage methods which employ an external LM.
Darwin Bautista, Rowel Atienza
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Text extraction and recognition method for license plates [PDF]
E3S Web of Conferences, 2023 Text extraction from images has always been challenging, especially if the image is taken under bad conditions, like lightning and noise that can influence text detection and recognition.
Moussaoui Hanae +2 more
doaj +1 more source
SVTR: Scene Text Recognition with a Single Visual Model [PDF]
International Joint Conference on Artificial Intelligence, 2022 Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient.
Yongkun Du +7 more
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Revisiting Scene Text Recognition: A Data Perspective [PDF]
IEEE International Conference on Computer Vision, 2023 This paper aims to re-assess scene text recognition (STR) from a data-oriented perspective. We begin by revisiting the six commonly used benchmarks in STR and observe a trend of performance saturation, whereby only 2.91% of the benchmark images cannot be
Qing-Yuan Jiang +4 more
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SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition [PDF]
Computer Vision and Pattern Recognition, 2022 End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition.
Mingxin Huang +8 more
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Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023 Capturing images is a key part of automation for high-level tasks such as scene text recognition. Low-light conditions pose a challenge for high-level perception stacks, which are often optimized on well-lit, artifact-free images.
Cindy M. Nguyen +3 more
semanticscholar +1 more source
A Plant Disease Recognition Method Based on Fusion of Images and Graph Structure Text
Frontiers in Plant Science, 2022 The disease image recognition models based on deep learning have achieved relative success under limited and restricted conditions, but such models are generally subjected to the shortcoming of weak robustness. The model accuracy would decrease obviously
Chunshan Wang +16 more
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An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition.
Baoguang Shi, X. Bai, C. Yao
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Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition [PDF]
ACM Multimedia, 2022 Existing text recognition methods usually need large-scale training data. Most of them rely on synthetic training data due to the lack of annotated real images.
Mingkun Yang +7 more
semanticscholar +1 more source
Multi-Granularity Prediction for Scene Text Recognition [PDF]
European Conference on Computer Vision, 2022 Scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this challenging problem, numerous innovative methods have been successively proposed and incorporating linguistic knowledge into STR models has ...
P. Wang, Cheng Da, C. Yao
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