Datasets:

pulse-ai
/

PulseBench-Select

license: cc-by-nc-nd-4.0
task_categories:
  - image-to-text
  - document-question-answering
language:
  - en
size_categories:
  - n<1K
tags:
  - selection-detection
  - checkbox-detection
  - benchmark
  - document-ai
  - selection-f1
  - ocr
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
  - config_name: results
    data_files:
      - split: train
        path: results/train-*.parquet

PulseBench-Select

A benchmark for selected-option detection in document images.

PulseBench-Select contains 485 cleaned document images with ground-truth annotations for checkboxes, radio buttons, and marked answer choices. Each sample pairs a document image with public ground truth for the visible options that are selected.

Scoring methodology (GitHub): https://github.com/Pulse-Software-Corp/PulseBench-Select

Quick Start

from datasets import load_dataset
import json

# Load benchmark data: document images plus cleaned ground truth.
ds = load_dataset("pulse-ai/PulseBench-Select")

sample = ds["train"][0]
sample["sample_id"]                    # Public sample id
sample["image"]                        # PIL image of the document page
gt = json.loads(sample["ground_truth"])
gt["selected_items"]                   # Selected options used for scoring

# Load aggregate benchmark results.
results = load_dataset("pulse-ai/PulseBench-Select", "results")

row = results["train"][0]
row["display_name"]                    # Provider display name
row["selection_f1"]                    # Corpus-pooled Selection F1

Dataset Overview

Split	Samples	Page Annotations	Selected Items	Selection Candidates
train	485	14,516	1,976	4,180

The benchmark focuses on pages where systems must determine which visible options are selected. Ground-truth coordinates are normalized eight-point polygons in reading order: [x0, y0, x1, y1, x2, y2, x3, y3].

459 samples contain at least one selected item; 26 samples contain no selected items and are retained to measure false positives.

Scoring: Selection F1

Selection F1 evaluates only the positive selected class.

Parse ground truth and predictions into selected-item records with sample_id, page, content, bbox, and selected.
Match each predicted selected item to the best unmatched ground-truth selected item on the same sample and page.
Filter matches with content token overlap >= 0.80. If both items include 8-point bounding boxes, the bbox centroid distance must also be <= 0.35 in normalized page units.
Score matched selected items as true positives, unmatched predictions as false positives, and unmatched ground-truth items as false negatives.
Report corpus-pooled micro precision, recall, and F1, along with per-sample macro diagnostics.

Token overlap is computed as:

|tokens(ground_truth) intersect tokens(prediction)| / max(|tokens(ground_truth)|, |tokens(prediction)|)

The bbox centroid check is a veto used to prevent repeated labels with identical text, such as multiple Yes or No options on the same page, from matching the wrong spatial item. The public scorer supports disabling this check with --centroid-max -1.

Results

We evaluated 6 systems using Selection F1. Scores below are corpus-pooled micro precision, recall, and F1 from the benchmark run associated with this release.

Rank	Provider	Precision	Recall	Selection F1
1	Pulse	0.782	0.761	0.772
2	GPT-5.5	0.383	0.311	0.343
3	Gemini 3.1 Pro	0.334	0.317	0.325
4	Gemini 3.5 Flash	0.317	0.311	0.314
5	Claude Opus 4.8	0.307	0.293	0.300
6	GPT-4o	0.223	0.191	0.206

The results config includes these aggregate results plus macro precision, macro recall, and skipped-sample counts for each provider.

Schema

Default config

Column	Type	Description
`sample_id`	string	Stable public sample identifier
`image`	image	Document image
`ground_truth`	string	JSON with `page_count`, `annotations`, and `selected_items`
`annotation_count`	int	Number of cleaned page annotations
`selected_count`	int	Number of selected ground-truth items
`selection_candidate_count`	int	Number of annotations or cells containing visible selection marks
`selection_stats`	string	JSON summary for the row

Results config

Column	Type	Description
`rank`	int	Rank by corpus-pooled Selection F1
`provider`	string	Provider identifier
`display_name`	string	Provider display name
`precision`	float	Corpus-pooled positive-class precision
`recall`	float	Corpus-pooled positive-class recall
`selection_f1`	float	Corpus-pooled positive-class F1
`macro_precision`	float	Mean per-sample precision over scored samples
`macro_recall`	float	Mean per-sample recall over scored samples
`macro_skipped_samples`	int	Samples skipped from macro averaging because precision or recall was undefined
`metric_version`	string	Metric version used for the reported row

Ground Truth Format

{
  "page_count": 1,
  "annotations": [
    {
      "category": "List-item",
      "bbox": [0.08, 0.33, 0.20, 0.33, 0.20, 0.35, 0.08, 0.35],
      "content": "B) Example option",
      "page": 1,
      "selected": true,
      "selection_candidate": true
    }
  ],
  "selected_items": [
    {
      "page": 1,
      "bbox": [0.08, 0.33, 0.20, 0.33, 0.20, 0.35, 0.08, 0.35],
      "content": "B) Example option",
      "category": "List-item"
    }
  ]
}

License

This dataset is released under CC BY-NC-ND 4.0.