Whiteboards

A small, single-author whiteboard corpus for evaluating vision-language models on handwritten OCR accuracy and for studying pseudotext hallucination — the failure mode where a VLM invents plausible-but-wrong words for ambiguous handwriting.

Every image is the same wall-mounted whiteboard, same marker, same author, photographed with a phone. This is deliberate: the dataset exists to measure whether a small number of human-authored ground-truth pairs can improve transcription on a specific personal whiteboard, not to train a general-purpose OCR system.

• 21 samples, ~150 KB WebP each, 4096×3072.
• Fields: file_name, id, category, transcription, description.
• Split: all rows are in test. Dataset is too small to train a split on.
• Source repo: https://github.com/danielrosehill/Whiteboard-OCR-Few-Shot-Learning

Samples

One example per category:

Prose (01)	List (04)	Table (07)

Flowchart (08)	Diagram (13)	Mixed (19)

Fields

Field	Type	Description
file_name	string	Path to image (relative to data/).
id	string	Two-digit sample id (01…21).
category	string	Coarse content type — see below.
transcription	string	Human-authored verbatim ground truth (markdown, with arrows/symbols preserved). This is the primary OCR target.
description	string	Human-authored prose description of the board, for tasks that need semantic/structural grounding rather than verbatim text.

Categories

Category	Count	What it means
prose	2	Handwritten sentences / paragraphs, no structure.
list	6	Bulleted, numbered, or indented hierarchical notes.
table	2	Row/column tabular content (calendar, capitals).
flowchart	1	Decision tree with yes/no branches.
diagram	9	Boxes-and-arrows / agent-workflow / architecture sketches.
mixed	1	Combinations (e.g., two-column with text + sketch).

Envisioned evaluation tasks

1. Zero-shot OCR accuracy (image → text)

Prompt a VLM with each image and a generic instruction ("transcribe this whiteboard verbatim, preserving arrows and symbols"). Score the output against transcription.

Suggested metrics:

• CER (character error rate) on the verbatim text.
• Pseudotext count — number of tokens in the prediction that do not appear in, and are not a plausible symbol-rendering of, the ground truth. This is the metric the dataset was built to measure.

2. Few-shot OCR (in-context grounding)

Hold out N samples. Show the model the other 21−N as (image, transcription) pairs in the prompt, then ask it to transcribe the held-out ones. Does CER and pseudotext count drop vs. zero-shot? This is the dataset's core hypothesis.

3. Image-to-image preservation (whiteboard → clean diagram)

Feed each image to an image-edit model (e.g. Gemini 2.5 Flash Image / Nano Banana, Flux Kontext, Qwen-Image-Edit) with a prompt like "redraw as a clean tech diagram." OCR the output, diff against transcription.

Metric: does the stylistic rewrite preserve text, or introduce pseudotext? Many image-edit models will silently "fix" words they can't read, producing plausible but wrong diagrams — this dataset lets you quantify that.

4. Structure/semantics tasks (using description)

• Visual question answering: ask the model questions whose answers require reading the board (e.g. "what does the bot ask the user?" for sample 13).
• Captioning evaluation: generate a description, compare to the human-authored description field with an LLM-as-judge.
• Category classification: predict category from the image alone.

5. Handwriting-specific probes

• Abbreviation handling: w/, &, ppl, etc. appear throughout.
• Arrow / symbol rendering: →, ↳, ↔, ↗ appear in ground truth.
• Number/technical token OCR: sample 18 has times (0900, 1500), sample 20 has subscripted agent names (A_1, SA_2).

Quickstart

from datasets import load_dataset

ds = load_dataset("danielrosehill/Whiteboards", split="test")
print(ds[0]["transcription"])
ds[0]["image"]  # PIL.Image

Minimal zero-shot eval sketch:

from datasets import load_dataset
from jiwer import cer

ds = load_dataset("danielrosehill/Whiteboards", split="test")
preds = [your_vlm(row["image"]) for row in ds]
refs = [row["transcription"] for row in ds]
print("CER:", cer(refs, preds))

Known limitations

• Single author, single board, single marker. Not a general OCR benchmark — it's a probe for personal / few-shot grounding setups.
• No multi-color samples. All black marker.
• Free-form markdown transcriptions, not a structured nodes/edges schema. Diagram-structure evaluation needs a parser.
• Some samples overlap — e.g. 14/15/16 are progressive close-ups of the same diagram. This is deliberate (tests whether crop affects OCR) but should be accounted for in splits.
• Personal content — agent-workflow sketches, a calendar, food/drink preferences. No high-sensitivity PII, but review before redistributing downstream.

Citation

@misc{whiteboards2026,
  author = {Rosehill, Daniel},
  title  = {Whiteboards: a small VLM OCR / pseudotext evaluation set},
  year   = {2026},
  url    = {https://huggingface.co/datasets/danielrosehill/Whiteboards}
}

License

CC-BY-4.0.