Scene text recognition has been a hot topic in computer vision. Recent methods adopt the
attention mechanism for sequence prediction which achieve convincing results. However …

Automatic handwriting recognition systems are of interest for academic research fields and
for commercial applications. Recent advances in deep learning techniques have shown …

The state-of-the-art methods usually integrate with linguistic knowledge in the recognizer,
which makes models more complicated and hard for resource-lacking languages. This letter …

String recognition is one of the most important tasks in computer vision applications.
Recently the combinations of convolutional neural network (CNN) and recurrent neural …

End-to-end Frameworks with Connectionist Temporal Classification (CTC) have achieved
great success in text recognition. Despite high accuracies with deep learning, CTC-based …

String recognition is one of the challenging tasks in document analysis and recognition
areas. Recently, with the surge of interest in end-to-end segmentation-free methods, CRNN …

The sequential labeling model is commonly used for time series or sequence data where
each instance label is classified using previous instance label. In this work, a sequential …

Connectionist Temporal Classification (CTC) is an objective function for sequence learning
and has shown promising results in speech and text recognition tasks. However, its inherent …

Scene text recognition has drawn great attentions in the community of computer vision and
artificial intelligence due to its challenges and wide applications. State-of-the-art recurrent …

Recognition of isolated handwritten digits is a well-studied research problem and several
models show high recognition accuracy on different standard datasets. But the same is not …