RNN based Deep learning models has shown tremendous success in sequential and
temporal data where the order is critical to achieve higher accuracy in context …

This paper presents an attention-based row-column encoder-decoder (ARCED) model for
recognizing an input image of multiple text lines from Japanese historical documents without …

Offline handwritten text recognition is still a big challenging problem due to various
backgrounds, noises, diversity of writing styles, and multiple touches between characters. In …

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

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 …

Handwritten Japanese text recognition is still a big challenging task due to the large
character set, diversity of writing styles, and multiple-touches between characters. In this …

This paper focuses on ancient Japanese Mokkan (woodcuts) to discuss the background,
current status and issues in the digitisation of cultural heritage, including matters of research …

Offline handwritten text recognition is an important part of document analysis and it has been
receiving a lot of attention from numerous researchers for decades. In this paper, we present …

World War I proved air forces to be a critical driver for warfare outcomes. Mastery of the latest
technology appeared to be crucial to the battle's success. The combat capabilities of …

World War I proved air forces to be a critical driver for warfare outcomes. Mastery of the latest
technology appeared to be crucial to the battle's success. The combat capabilities of …