In this paper, we present HAnnoI – short for Handwriting Annotation Interface –, an open-source GUI application developed in Python that allows its users to identify and annotate so-called Regions of Interest (ROIs) within digital images. Several meta data such as their coordinates are retained for each ROI and they can be annotated on user-defined annotation layers. HAnnoI comes with a function to export all annotations to a CSV file, enabling further processing as well as quantitative analyses. HAnnoI also has a function to extract single PNG image files of all ROIs. It was developed to mark and annotate single letters in scans of handwritten (alphabetic) texts for linguistic analyses, yet it is not limited to this particular use case. In this paper, we first provide information on HAnnoI’s conception and technical details as well as an overview of alternative applications. We then showcase HAnnoI’s capabilities in a letter annotation task, where five annotators marked instances of lower case <s> in handwritten texts. Finally, we report on an exploratory analysis of this data, showing what kinds of investigations are enabled by using HAnnoI. The tool is available for free use at https://github.com/pywielR/HAnnoI.

Chinese verb-resultative complement construction (VRCC), constitute a distinctive syntactic-semantic pattern in Chinese that integrates agent-patient dynamics with real-world state changes; yet widely used benchmarks such as CLiMP and ZhoBLiMP provide few minimal-pair probes tailored to these constructions. We introduce ZhVrcMP, a 1,204 pair dataset spanning two paradigms: resultative complement presence versus absence, and verb–complement order. The examples are drawn from Modern Chinese and are annotated for linguistic validity. Using mean log probability scoring, we evaluate Zh-Pythia models (14M-1.4B) and Mistral-7B-Instruct-v0.3. Larger Zh-Pythia models perform strongly, especially on the order paradigm, reaching 89.87% accuracy. Mistral-7B-Instruct-v0.3 shows lower perplexity yet overall weaker accuracy, underscoring the remaining difficulty of modeling constructional semantics in Chinese.