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Found 7 papers, 5 papers with code
Writer adaptation for offline text recognition: An exploration of neural network-based methods
In this paper, we explore how HTR models can be made writer adaptive by using only a handful of examples from a new writer (e. g., 16 examples) for adaptation.
Oil Spill Segmentation using Deep Encoder-Decoder models
Even though oil spills are in and themselves difficult to clean up, the first and foremost challenge is to detect spills.
LostPaw: Finding Lost Pets using a Contrastive Learning-based Transformer with Visual Input
In order to facilitate such an application, this study introduces a contrastive neural network model capable of accurately distinguishing between images of pets.
The Effects of Character-Level Data Augmentation on Style-Based Dating of Historical Manuscripts
Identifying the production dates of historical manuscripts is one of the main goals for paleographers when studying ancient documents.
Image-based material analysis of ancient historical documents
Researchers continually perform corroborative tests to classify ancient historical documents based on the physical materials of their writing surfaces.
Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)
Although many scholars believe that 1QIsaa was written by one scribe, we report new evidence for a breaking point in the series of columns in this scroll.
BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks
Handwritten document-image binarization is a semantic segmentation process to differentiate ink pixels from background pixels.
