Add dataset card

README.md

@@ -1,4 +1,22 @@
 ---
+license: cc-by-sa-4.0
+language:
+- nl
+- fr
+- de
+- en
+task_categories:
+- image-to-text
+size_categories:
+- n<1K
+pretty_name: Greetings From! — historical postcard address transcription
+tags:
+- glam
+- handwritten-text-recognition
+- htr
+- ocr-evaluation
+- postcards
+- information-extraction
 dataset_info:
   features:
   - name: card_index
@@ -45,3 +63,177 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# Greetings From! — historical postcard address transcription
+
+500 handwritten address regions cropped from the backs of historical picture postcards sent within and
+between Belgium, France, Germany, Luxembourg, the Netherlands and the UK. Each region carries a
+human-corrected ground-truth transcription, the HTR output it was corrected from, and GPT-4 structured
+address extractions run over both.
+
+Created by Thomas Smits, Wouter Haverals, Loren Verreyen, Mona Allaert and Mike Kestemont for
+[*Greetings from! Extracting address information from 100,000 historical picture
+postcards*](https://ceur-ws.org/Vol-3558/paper6180.pdf) (CHR 2023), and deposited on Zenodo
+([10.5281/zenodo.10005566](https://doi.org/10.5281/zenodo.10005566)).
+
+This repository is a format conversion of that deposit — the same images and transcriptions, cut into
+one row per postcard.
+
+## How the data was produced
+
+The 500 are a random sample from a corpus of ~102,000 postcards hosted on
+[Delcampe](https://www.delcampe.net/), passed through a three-stage pipeline:
+
+| Stage | Method | Reported performance |
+|---|---|---|
+| Locate the address region on the card back | YOLOv8 | mAP50 0.94, mAP50-95 0.72 |
+| Transcribe it | Transkribus **Text Titan I** | CER 7.62% (measured with CERberus) |
+| Structure the address | GPT-4 | 419 of 500 geocodable coordinates |
+
+Ground truth was made by five human annotators correcting the HTR output, not transcribing from
+scratch.
+
+## The ground truth is address-only
+
+> [!WARNING]
+> `gt_text` is **not** a full corrected transcription. The annotators systematically corrected only the
+> text carrying geographical address information. **49.4% of ground-truth lines (1,219 of 2,469) carry a
+> `*` or `@` prefix**, marking them as outside that scope — roughly half the ground truth is
+> uncorrected HTR.
+
+The paper states the convention:
+
+| Marker | Meaning |
+|---|---|
+| `*` line prefix | line without address information (e.g. a person's name) — **not corrected** |
+| `@` line prefix | irrelevant line — **not corrected** |
+| `#` | unreadable character |
+
+So computing CER between `gt_text` and `htr_text` over the whole string measures partly against
+uncorrected HTR, and will understate the real error rate. To reproduce the paper's 7.62%, score only
+the unprefixed lines:
+
+```python
+def address_lines(text):
+    return [l for l in text.splitlines() if not l.startswith(("*", "@"))]
+```
+
+The rule is not absolute — some prefixed lines were corrected anyway. Card 0's addressee reads
+`J Bath` in the HTR and `*J Buth` in the ground truth, so a starred line was edited despite carrying no
+address information. Treat `*`/`@` as "outside the systematic correction pass", not as a guarantee that
+the line is byte-identical to the HTR output.
+
+## Structure
+
+One row per postcard, 500 rows, single `train` split.
+
+| Field | Notes |
+|---|---|
+| `card_index` | 0–499, stable, ordered by composite then reading order |
+| `gpt4_order` | the `Order` key from the GPT-4 files; `-1` where no record exists (5 cards) |
+| `composite_id`, `region_id` | position in the source composite sheet |
+| `image` | the cropped address region (JPEG, quality 92) |
+| `bbox` | `[x, y, w, h]` of the crop within the source composite |
+| `gt_text` / `htr_text` | region-level transcription, both sides |
+| `gt_lines` / `htr_lines` | the same, split into PAGE text lines |
+| `gt_address_json` / `htr_address_json` | raw GPT-4 output as a JSON string; empty where absent |
+| `gt_city`, `gt_country`, `htr_city`, `htr_country` | convenience fields lifted from the JSON |
+
+`gt_text` and `htr_text` are aligned by construction — the two source archives contain the same 5
+composites (byte-identical JPEGs) with the same 100 regions each, so every row's two transcriptions
+describe the same pixels.
+
+## Caveats
+
+**The GPT-4 output has no fixed schema.** It is free-form LLM output, not structured fields. Across the
+GT file 17 distinct keys appear; across the HTR file, 27 — including `Message`, `Date`, `Province`,
+`State`, `Place of Interest`, `City/Village Name 2`, `Additional 7`. That is why the full output is
+kept as a JSON string and only the two most consistent fields are lifted into columns. Do not assume
+`gt_city` is populated: it is empty wherever GPT-4 did not emit that key.
+
+**GPT-4 hallucinates plausible corrections.** The paper documents it silently changing `Junda` to
+`Zundert`, and adding `France` as the country for Courcelles, which is in Belgium. These are
+LLM outputs, not verified addresses — treat them as a system's predictions, not as ground truth.
+
+**Five cards have no GPT-4 record.** The deposit's GPT-4 files hold 495 entries for 500 regions. Those
+five rows have `gpt4_order = -1` and empty address fields: `c2/region_19`, `c2/region_89`,
+`c4/region_26`, `c5/region_3`, `c5/region_84`.
+
+**The join was reconstructed, not documented.** The `Order` field is not `region_index + 1` — because
+five regions are missing, the offset accumulates unevenly through the sequence. A uniform per-composite
+shift gets 100% / 70% / 94% / 69% / 81%: close enough to look right, wrong enough to mislabel rows.
+The alignment here was recovered with a monotonic dynamic-programming match on normalised address
+tokens, giving exactly 5 skips and an 88% token-match rate at aligned positions against a ~1% rate for
+shifted controls. It is recomputed at build time and the build aborts if it degrades. It is still an
+inference, and a handful of the 12% non-matching rows may be misaligned rather than simply cases where
+GPT-4 emitted a garbled or empty city.
+
+**Five cards have no HTR text at all**, and five have no ground truth. **472 of the 500 rows carry at
+least one address-bearing line on both sides** — that is the subset usable for a paired transcription
+comparison. Median length is comparable across the two sides (60 vs 59 characters), as you would expect
+for a correction pass rather than a re-transcription.
+
+**These are address regions, not whole postcards.** The crops are YOLOv8-detected address areas from
+the card backs. Picture fronts are not in the deposit, and neither is any message text outside the
+address region.
+
+**If you go back to the original deposit:** the GT PAGE XML carries region-level `TextEquiv`, but the
+HTR export does not — its text lives only on `TextLine`. Reading region-level text on both sides yields
+500 silently empty HTR strings. Here both sides are built by joining the line-level text, which
+reproduces the GT region string exactly.
+
+**Language is mixed and unlabelled.** Cards span six countries and the deposit carries no per-card
+language field. Handwriting, spelling and place-name conventions vary accordingly — the paper
+attributes the relatively high CER to exactly this "hyper-diversity".
+
+## Load
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("davanstrien/greetings-from-postcards", split="train")
+
+# HTR error on the address lines only — the part that was actually corrected
+scored = ds.filter(lambda r: r["gt_text"].strip() and r["gpt4_order"] > 0)
+```
+
+## Licence
+
+CC BY-SA 4.0, following the upstream deposit. Share-alike: anything derived from this and redistributed
+carries the same licence.
+
+The postcards themselves are held by [Delcampe](https://www.delcampe.net/) sellers and collectors; the
+deposit licenses the transcriptions and the cropped regions as distributed. The authors ask that the
+paper be cited.
+
+## Credit
+
+Data created by Thomas Smits (University of Amsterdam), Wouter Haverals (Princeton University), Loren
+Verreyen, Mona Allaert and Mike Kestemont (University of Antwerp). Note that the Zenodo deposit lists
+Haverals alone as creator whilst the accompanying paper has five authors; the citation below follows
+the paper.
+
+Converted and repackaged for the Hub by [Daniel van Strien](https://huggingface.co/davanstrien).
+
+```bibtex
+@inproceedings{smits2023greetings,
+  author    = {Smits, Thomas and Haverals, Wouter and Verreyen, Loren and
+               Allaert, Mona and Kestemont, Mike},
+  title     = {{Greetings from! Extracting address information from 100,000 historical picture postcards}},
+  booktitle = {Proceedings of the Computational Humanities Research Conference (CHR 2023)},
+  series    = {CEUR Workshop Proceedings},
+  volume    = {3558},
+  pages     = {512--529},
+  year      = {2023},
+  address   = {Paris, France},
+  url       = {https://ceur-ws.org/Vol-3558/paper6180.pdf}
+}
+
+@dataset{haverals_2023_greetingsfrom,
+  author    = {Haverals, Wouter},
+  title     = {{Greetings From! Historical Postcards Address Transcription Dataset}},
+  year      = {2023},
+  publisher = {Zenodo},
+  doi       = {10.5281/zenodo.10005566}
+}
+```