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---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: texts
sequence: string
- name: bboxes_block
sequence:
sequence: float64
- name: bboxes_line
sequence:
sequence: float64
- name: categories
sequence: int64
- name: page_hash
dtype: string
- name: original_filename
dtype: string
- name: page_no
dtype: int64
- name: num_pages
dtype: int64
- name: image_width
dtype: int64
- name: image_height
dtype: int64
- name: collection
dtype: string
- name: doc_category
dtype: string
splits:
- name: train
num_bytes: 166529577.0
num_examples: 400
download_size: 162656888
dataset_size: 166529577.0
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- image-segmentation
---
**Please see paper & code for more information:**
- https://github.com/mbzuai-oryx/KITAB-Bench
- https://arxiv.org/abs/2502.14949
KITAB-Bench is a comprehensive multi-domain benchmark for Arabic OCR and document understanding. It includes tasks such as text recognition (OCR), layout detection, line detection and recognition, table recognition, PDF-to-markdown conversion, chart-to-dataframe conversion, diagram-to-JSON conversion, and visual question answering (VQA). The dataset contains a wide range of document types from various domains, with high-quality human-labeled annotations. See the [project website](https://mbzuai-oryx.github.io/KITAB-Bench/) for more details. |