English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

On-line handwritten character segmentation is often associated withhandwriting recognition and even though recognition models include mechanismsto locate relevant positions during the recognition process, it is typicallyinsufficient to produce a precise segmentation. Decoupling the segmentationfrom the recognition unlocks the potential to further utilize the result of therecognition. We specifically focus on the scenario where the transcription isknown beforehand, in which case the character segmentation becomes anassignment problem between sampling points of the stylus trajectory andcharacters in the text. Inspired by the $k$-means clustering algorithm, we viewit from the perspective of cluster assignment and present a Transformer-basedarchitecture where each cluster is formed based on a learned character query inthe Transformer decoder block. In order to assess the quality of our approach,we create character segmentation ground truths for two popular on-linehandwriting datasets, IAM-OnDB and HANDS-VNOnDB, and evaluate multiple methodson them, demonstrating that our approach achieves the overall best results.

Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation