Advancing Handwritten Text Detection by Synthetic Text

Markus Muth, Marco Peer, Florian Kleber, Robert Sablatnig

Abstract: In this paper, the usability of synthetic handwritten text to improve machine learning models is examined for the domain of handwritten text detection. We generate synthetic handwritten text by using an existing model based on a style conditioned GAN, and add those texts to scanned documents to mimic handwritten annotations. Object detection models (YOLOv5 and YOLOv8) are trained using synthetic data as a baseline to distinguish handwritten text from remaining content. We study different granularity labels (word-, line- and paragraph-level) and model sizes in our evaluation and show that applying those models to real data results in a mAP@50 of 0.88 and a pixel-level F1@50 of 0.96 for the CVL dataset, and a mAP@50 of 0.72 and F1@50 of 0.89 for SCAN, a custom dataset, created by adding real handwritten annotations to a scientific paper.

External IDs:dblp:conf/icpr/MuthPKS24