Recent Swedish OCR efforts rely primarily on traditional OCR methods, including deep CNN–LSTM hybrid neural networks and transformer-based models. Some approaches have also demonstrated the applicability of VLM-driven OCR to historical material. However, to date, no studies have examined in depth the performance of VLM-based OCR on historical Swedish sources. In this paper, we ask: How do transformers and VLMs differ in character- and word-level recognition performance across typefaces, and what qualitative differences can be observed in their error patterns? We show that fine-tuned versions of the Alibaba Cloud Qwen3-VL-8B-Instruct and Qwen3-VL-2B-Instruct, combined with a simple repetition-trimming step, outperform conventional OCR systems. Remaining errors are primarily attributable to challenges associated with the Blackletter typeface and formatting issues, such as missing or extra line breaks, characters, and spaces. Even when characters are correctly recognized, formatting inconsistencies can substantially increase transcription error rates.

This paper describes a novel experimental setup exploiting state-of-the-art capture equipment to collect a multimodally rich game-solving collaborative multiparty dialogue corpus. The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. The participants were paired into teams based on their degree of extraversion as resulted from a personality test. With the participants sits a tutor that helps them perform the task, organizes and balances their interaction and whose behavior was assessed by the participants after each interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, together with manual annotations of the tutor’s behavior constitute the Tutorbot corpus. This corpus is exploited to build a situated model of the interaction based on the participants’ temporally-changing state of attention, their conversational engagement and verbal dominance, and their correlation with the verbal and visual feedback and conversation regulatory actions generated by the tutor.