AI 4 C ulture platform: upskilling experts on multilingual / -modal tools (2025)

Abstract: The AI4Culture project, funded by the European Commission (2023-2025), developed a platform (https://ai4culture.eu) to educate cultural heritage (CH) professionals in AI technologies. Acting as an online capacity building hub, the platform describes openly labeled data sets and deployable and reusable tools applying AI technologies in tasks relevant to the CH sector. It also offers tutorials for tools and recipes for the combination of tools. In addition, the platform allows users to contribute their own resources. The resources described by project partners involve applications for optical or handwritten character recognition (OCR, HTR), generation and validation of subtitles, machine translation, image analysis, and semantic linking. The partners customized various tools to enhance the usability of interfaces and components. Here, we zoom in on the use case of correcting OCR/HTR output using various means (such as an unstructured manual transcription) to facilitate multilingual accessibility and create structured ground truth (text lines with image coordinates).

## customization existing software has been customized in various ways: (1) fbk and translated set up an opensource automatic subtitling system @xcite ; (2) pangeanic combined computer-aided translation functionalities with ch-oriented mt engines; (3) ntua and datoptron extended their tools for semantic enrichment and validation of metadata and integrated them with the ch data space @xcite ; (4) datable provided an object and color detection tool; (5) crosslang added an open source htr tool to its occam transcription and translation environment foot_0 and func-tionality for automatic correction of ocr/htr output and thus improved mt of transcriptions @xcite , and (6) ait interlinked transcribathon foot_1 with the occam services. regarding ocr/htr, the two correction approaches reported by @xcite (using a lexicon and language models) were extended towards the project end with a method matching output to an existing unstructured manual transcription (i.e. flat text). a final manual validation of the result of the approaches (structured, i.e. text lines with image coordinates) leads to ground truth useful for training new models or fine-tuning existing ones. while the first two approaches show variable performance (especially for low initial output quality), the third one shows clear results, as illustrated in figure 2 , where the (approximate) correspondence between lines in the image and a very long line in the manual transcription is detected. this "recycling" technique allows for reaching a minimally low cer score (character error rate), even for very poor original output quality.