Search Results for author:
Found 5 papers, 2 papers with code
Manifold Mixup improves text recognition with CTC loss
Modern handwritten text recognition techniques employ deep recurrent neural networks.
Adversarial Generation of Handwritten Text Images Conditioned on Sequences
State-of-the-art offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained.
Are 2D-LSTM really dead for offline text recognition?
In this work, we aim at a fair comparison between 2D and competing models and also extensively evaluate them on more complex datasets that are more representative of challenging "real-world" data, compared to "academic" datasets that are more restricted in their complexity.
Full-Page Text Recognition: Learning Where to Start and When to Stop
Text line detection and localization is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents.
Learning to detect and localize many objects from few examples
The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power.
