Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation
This work addresses a specific bottleneck in handwriting analysis for applications like document digitization, but it is incremental as it builds on existing Transformer and clustering ideas.
The paper tackles the problem of on-line handwritten character segmentation by decoupling it from recognition and framing it as an assignment problem between stylus trajectory points and known text characters, achieving the best overall results on IAM-OnDB and HANDS-VNOnDB datasets.
On-line handwritten character segmentation is often associated with handwriting recognition and even though recognition models include mechanisms to locate relevant positions during the recognition process, it is typically insufficient to produce a precise segmentation. Decoupling the segmentation from the recognition unlocks the potential to further utilize the result of the recognition. We specifically focus on the scenario where the transcription is known beforehand, in which case the character segmentation becomes an assignment problem between sampling points of the stylus trajectory and characters in the text. Inspired by the $k$-means clustering algorithm, we view it from the perspective of cluster assignment and present a Transformer-based architecture where each cluster is formed based on a learned character query in the Transformer decoder block. In order to assess the quality of our approach, we create character segmentation ground truths for two popular on-line handwriting datasets, IAM-OnDB and HANDS-VNOnDB, and evaluate multiple methods on them, demonstrating that our approach achieves the overall best results.