1 code implementation • 29 Mar 2023 • Konstantina Nikolaidou, George Retsinas, Vincent Christlein, Mathias Seuret, Giorgos Sfikas, Elisa Barney Smith, Hamam Mokayed, Marcus Liwicki
Our proposed method is able to generate realistic word image samples from different writer styles, by using class index styles and text content prompts without the need of adversarial training, writer recognition, or text recognition.
Ranked #1 on HTR on IAM
1 code implementation • 8 Feb 2023 • Gustav Grund Pihlgren, Konstantina Nikolaidou, Prakash Chandra Chhipa, Nosheen Abid, Rajkumar Saini, Fredrik Sandin, Marcus Liwicki
Deep perceptual loss is a type of loss function in computer vision that aims to mimic human perception by using the deep features extracted from neural networks.
no code implementations • 16 Mar 2022 • Konstantina Nikolaidou, Mathias Seuret, Hamam Mokayed, Marcus Liwicki
However, because of the very large variety of the actual data (e. g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task.
2 code implementations • LREC 2022 • Tosin P. Adewumi, Roshanak Vadoodi, Aparajita Tripathy, Konstantina Nikolaidou, Foteini Liwicki, Marcus Liwicki
The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work.