Extracting Toponyms from Maps of Jerusalem

Project Timeline

Timeframe	Task	Completion
Week 4	Finalize and present project proposals. Toponym extraction project selected.	✓
Week 5	Survey SOTA toponym extraction tools.	✓
Week 6	Port MapKurator's Spotter tool and model weights into Windows-based Python. Select two (later four) maps to use when implementing, evaluating, and fine-tuning MapKurator's model.	✓
Week 7	Create ground truth labels for first map with VIA's online interface.	✓
Week 8	Create ground truth labels for second map. Implement 1:1-matched precision and recall via IoU (geometry) and normalized Levenshtein (text). Calculate baseline accuracy statistics.	✓
Week 9	Implement multi-layer pyramid application of MapKurator's Spotter.	✓
Week 10	Create ground truth labels for third map. Implement toponym rectification and amalgamation on pyramid-derived toponyms.	✓
Week 11	Calculate pyramid accuracy statistics. Fine-tune toponym rectification and amalgamation. Deliver Midterm presentation.	✓
Week 12	Launch Wiki. Group words into toponyms via polygon size and location. Apply NLP tools to correct toponyms based on MapKurator strategy.
Week 13	Create ground truth labels for fourth map. Calculate final accuracy statistics. Hierarchize final toponyms and develop Voronoi map.
Week 14	Prototype toponym-disagreement visualizer. Finalize Wiki and deliver presentation.

Introduction & Motivation

A sample of the linguistic, geometrical, and typographical diversity in 19th-century maps of Jerusalem.

Methodology

Pyramid

Text Recitification

- Let $G_{j,k}$ represent subset $k$ of ground truth label $G_j$. Note that because $G_{j,k}$ is not defined as a proper subset, it is possible that $G_{j,k} = G_j$. Now, let the set $S_{j,k} = \{L_1, L_2, ..., L_{p_{j,k}}\}$ refer to the $p_{j,k}$ extracted labels corresponding to $G_{j,k}$ entirely. The goal of the Text Rectification stage is to retain the single most accurate extracted label $L_i$ in a given $S_{j,k}$ and exclude the rest—filtering $S_{j,k}$ to maintain just one 'representative' for $G_{j,k}$.

- To organize extracted labels into sets $S_{j,k}$:

 - Vectorize each extracted bounding box $P_i$ based on their bottom-left and top-right Cartesian coordinates.
 - Implement DBSCAN on the resulting four-dimensional vectors. 
 - \textcolor{red}{Include DBSCAN hyperparameters. Confirm if outliers are still allocated to individual $S_{j,k}$.}

- To filter $S_{j,k}$ collections to their most appropriate representatives:

 - Attempt to retain the label within $S_{j,k}$ with the highest $C_i$.
 - \textcolor{red}{Further detail on the RANSACK process is needed.}

- Let $\sigma_{j,k}^{*}$ represent the single label from set $S_{j,k}$ after Text Rectification has been completed.

Word Combination

Single Line

Multiple Lines

Curved Line

Github Repository

Jerusalem Maps EPFL DH405

References

Literature

• Kim, Jina, et al. "The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps." arXiv preprint arXiv:2306.17059 (2023).
• Li, Zekun, et al. "An automatic approach for generating rich, linked geo-metadata from historical map images." Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020

Webpages

• MapKurator GitRepo
• MapKurator Introduction