README.md

---
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
    dtype: int32
  - name: gpt4_order
    dtype: int32
  - name: composite_id
    dtype: int32
  - name: region_id
    dtype: string
  - name: image
    dtype: image
  - name: bbox
    list: int32
  - name: gt_text
    dtype: string
  - name: htr_text
    dtype: string
  - name: gt_lines
    list: string
  - name: htr_lines
    list: string
  - name: gt_address_json
    dtype: string
  - name: htr_address_json
    dtype: string
  - name: gt_city
    dtype: string
  - name: gt_country
    dtype: string
  - name: htr_city
    dtype: string
  - name: htr_country
    dtype: string
  splits:
  - name: train
    num_bytes: 48341189
    num_examples: 500
  download_size: 48247682
  dataset_size: 48341189
configs:
- config_name: default
  data_files:
  - 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("biglam/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}
}
```