
Dataset of synthetic map images in English for the ICDAR'25 Competition on Historical Map Text Detection, Recognition, and Linking. Annotations and images follow the format described at the competition website. Please refer to [1] for the generation process and usage. We extend [1] to provide grouping labels for location phrases. Train Annotations en25synth_train.json Images train.zip Files en25synth/train/*.jpg Tiles 35,000 Map Sheets - Words 348,494 Label Groups 157,483 Label Groups (Group Size > 1) 133,955 Illegible Words 0 Truncated Words 0 Valid Words 348,494 [1] Lin, Y., & Chiang, Y. -Y. (2024). Hyper-local deformable transformers for text spotting on historical maps. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5387-5397).
| 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 |
