Datasets:

LiZZieeee
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HighlightBench

Tasks:

Visual Question Answering

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Tags:

table-question-answering

table-understanding

markup-driven-understanding

visual-document-understanding

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HighlightBench

HighlightBench is a diagnostic benchmark for markup-driven table understanding that decomposes evaluation into five task families. It covers table QA over visual emphasis such as highlights, underlines, bold text, color annotations, missing entries, and structured table comparisons.

Files

highlightbench/real/qa.jsonl: real-table QA annotations
highlightbench/real/images/: real-table images
highlightbench/synthetic/qa.jsonl: synthetic-table QA annotations
highlightbench/synthetic/images/: synthetic-table images
highlightbench/highlightbench_all_qa.jsonl: combined QA annotations
highlightbench/score_qa.py: scoring script
highlight_generator/: minimal synthetic image generator
review.html: local visual review page

Each QA row keeps the compact fields needed for use, scoring, and subtask-level analysis:

{"dataset":"real","qid":"...","image_path":"...","task":"Constrained Retrieval","subtask":"Cell Retrieval","question":"...","answer":"..."}

Scoring

Prediction files only need qid and answer; task and subtask are metadata for analysis.

cd highlightbench
python3 score_qa.py --gt real/qa.jsonl --pred examples/pred_sample.real.jsonl --out examples/pred_sample.real.score.json
python3 score_qa.py --gt synthetic/qa.jsonl --pred examples/pred_sample.synthetic.jsonl --out examples/pred_sample.synthetic.score.json

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