
The FLOW project develops modular, microservice-based workflows for machine learning-driven data processing in the Digital Humanities. By separating key tasks like preprocessing, training, inference, and evaluation into independent components, it provides scalable solutions for automatic text recognition (ATR). Centered around GitHub and its CI/CD capabilities, these microservices support streamlined processes tailored to diverse research needs. Researchers can configure the services and trigger automated pipelines via GitHub Issues, without requiring any coding knowledge. The project aims to implement a real-world text recognition pipeline and demonstrate the architecture’s potential to lower technical barriers while supporting open science practices.
Machine Learning, GitHub, handwritten text recognition, ATR, microservice, Flow, GitHub actions, TrOCR, automatic text recognition, HTR
Machine Learning, GitHub, handwritten text recognition, ATR, microservice, Flow, GitHub actions, TrOCR, automatic text recognition, HTR
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
