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Found 6 papers, 3 papers with code
Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer
Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components.
TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers
Motivated by this, we suggest an approach to regulate the reliance on local statistics that improves text recognition performance.
Single Pair Cross-Modality Super Resolution
The network is trained on the two input images only, learns their internal statistics and correlations, and applies them to up-sample the target modality.
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design.
Blind Visual Motif Removal from a Single Image
Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image.
Clustering-driven Deep Embedding with Pairwise Constraints
In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering using a neural network.
