---
license: cc-by-nc-sa-4.0
task_categories:
- zero-shot-object-detection
- image-to-text
language:
- en
tags:
- referring-expression-comprehension
- visual-grounding
- text-object-grounding
- ocr
- scene-text
- autonomous-driving
- nuscenes
pretty_name: Read The Object
size_categories:
- 1K<n<10K
dataset_info:
- config_name: nuscenes_referring_expression
  features:
  - name: id
    dtype: string
  - name: source
    dtype: string
  - name: image
    dtype: image
  - name: image_bbox
    dtype: image
  - name: category
    dtype: string
  - name: obj_box
    list: float64
    length: 4
  - name: texts
    list: string
  - name: combined_text_box
    list: float64
    length: 4
  - name: ref_expression
    dtype: string
  - name: num_texts_used
    dtype: int32
  - name: sample_type
    dtype: string
  - name: attribute
    dtype: string
  splits:
  - name: test
    num_bytes: 573221130
    num_examples: 1735
  download_size: 552935523
  dataset_size: 573221130
- config_name: nuscenes_captioning
  features:
  - name: id
    dtype: string
  - name: source
    dtype: string
  - name: image
    dtype: image
  - name: caption
    dtype: string
  splits:
  - name: test
    num_bytes: 35505332
    num_examples: 259
  download_size: 35459986
  dataset_size: 35505332
- config_name: nuscenes_text_object_grounding
  features:
  - name: id
    dtype: string
  - name: source
    dtype: string
  - name: image
    dtype: image
  - name: image_bbox
    dtype: image
  - name: query_text
    dtype: string
  - name: categories
    list: string
  - name: obj_boxes
    list:
      list: float64
      length: 4
  splits:
  - name: test
    num_bytes: 673454364
    num_examples: 2107
  download_size: 673454364
  dataset_size: 673454364
- config_name: nuscenes_text_region_grounding
  features:
  - name: id
    dtype: string
  - name: source
    dtype: string
  - name: image
    dtype: image
  - name: image_bbox
    dtype: image
  - name: query_text
    dtype: string
  - name: boxes
    list:
      list: float64
      length: 4
  splits:
  - name: test
    num_bytes: 668216133
    num_examples: 2140
  download_size: 668216133
  dataset_size: 668216133
- config_name: nuscenes_ocr
  features:
  - name: id
    dtype: string
  - name: source
    dtype: string
  - name: image
    dtype: image
  - name: image_bbox
    dtype: image
  - name: texts
    list: string
  - name: boxes
    list:
      list: float64
      length: 4
  splits:
  - name: test
    num_bytes: 86364012
    num_examples: 259
  download_size: 86364012
  dataset_size: 86364012
configs:
- config_name: nuscenes_referring_expression
  data_files:
  - split: test
    path: nuscenes_referring_expression/test-*
- config_name: nuscenes_captioning
  data_files:
  - split: test
    path: nuscenes_captioning/test-*
- config_name: nuscenes_text_object_grounding
  data_files:
  - split: test
    path: nuscenes_text_object_grounding/test-*
- config_name: nuscenes_text_region_grounding
  data_files:
  - split: test
    path: nuscenes_text_region_grounding/test-*
- config_name: nuscenes_ocr
  data_files:
  - split: test
    path: nuscenes_ocr/test-*
---

# Read The Object

**Read The Object** is the official data release accompanying the paper
*"Read the Object: A Dataset and Task for Text-Grounded Object Understanding
in Real-World Images"* (Chowdhury, Park, Le, Chao, Hayat, Porikli, Mahajan —
The Ohio State University, Qualcomm AI Research, Boston University, York
University, Vector Institute for AI).

Objects in the real world are often distinguished not by visual appearance
alone but by the text printed on them — a traffic sign's route number, a
jersey number, a product label. The paper introduces **text–object
grounding**: the task of associating a piece of scene text with the exact
object instance it appears on, and shows that current object detectors,
OCR systems, and vision-language models all struggle at it despite doing
well on text recognition or object localization in isolation. The full
benchmark combines images from **OpenImages V5**, **MS COCO**, and
**nuScenes** (6,000 images, 25,208 referring-expression samples, 34,085
object instances, 137,246 text instances) and defines six complementary
tasks: Referring Expression Comprehension, Text–Object Grounding, OCR,
Text-Region Grounding, Visual Question Answering, and Multi-Text
Captioning.

**This repo currently contains only the nuScenes source category** of that
benchmark (the OpenImages V5 and COCO portions, plus the VQA and
Captioning tasks, are not yet part of this HF release — more will be
added incrementally). This is **benchmark/evaluation data, not a training
split** — every config below ships only a `test` split.

The dataset is organized by source, then by task, so future source
additions (e.g. COCO) slot in as sibling folders under the same layout:

```
read_the_object/
  <source>/
    images/                    clean source images (shared across tasks)
    referring_expression/      REC samples: referring_expression.json + images_bbox/
    captioning/                Captioning samples: captioning.json
    text_object_grounding/     TOG samples: text_object_grounding.json + images_bbox/
    text_region_grounding/     TRG samples: text_region_grounding.json + images_bbox/
    ocr/                       OCR samples: ocr.json + images_bbox/
```

Each `images_bbox/` image is drawn per-sample (red = target object box,
cyan = the specific text box(es) that sample's row refers to) — not every
box in the source image, only the ones relevant to that row. Captioning
has no `images_bbox/`, since its output is a whole-image caption rather
than a box.

For `datasets`/Dataset Viewer users, each task is also published as its own
Parquet config (embedded images, `test` split):

```python
from datasets import load_dataset
ds = load_dataset("act13/read_the_object", "nuscenes_referring_expression", split="test")
```

| config_name | task | rows |
|---|---|---|
| `nuscenes_referring_expression` | Referring Expression Comprehension | 1,735 |
| `nuscenes_captioning` | Multi-Text Captioning | 259 |
| `nuscenes_text_object_grounding` | Text–Object Grounding | 2,107 |
| `nuscenes_text_region_grounding` | Text–Region Grounding | 2,140 |
| `nuscenes_ocr` | OCR | 259 |

## `nuscenes/referring_expression/referring_expression.json` — Referring Expression Comprehension

Given a natural-language expression that includes both text and visual
attributes, locate the described object and its text region. One row per
referring-expression sample.

| field | type | description |
|---|---|---|
| `id` | string | unique sample id, e.g. `nuscenes_00000` |
| `source` | string | `"nuscenes"` |
| `image` | string | relative path to the clean image |
| `image_bbox` | string | relative path to the same image with just this sample's box(es) drawn |
| `category` | string | object category, `supercategory.subtype` (e.g. `vehicle.bus`, `signage.traffic_sign`) |
| `obj_box` | `[x1,y1,x2,y2]` | target object's bounding box, pixel coords |
| `texts` | `[string, ...]` | the scene-text string(s) used to build the expression |
| `combined_text_box` | `[x1,y1,x2,y2]` | bounding box of the text region(s) used (always contained within `obj_box`) |
| `ref_expression` | string | the referring expression, e.g. `"the bus with 'stop' written on it"` |
| `num_texts_used` | int | how many text instances were combined into this sample |
| `sample_type` | string | `single_text_single_object`, `multi_text_single_object`, or `disambiguated_same_category` |
| `attribute` | string (optional) | present only for `disambiguated_same_category` samples — a distinguishing visual/spatial attribute added because another object of the *same category* in the same image shares the same text (e.g. `"left blue"` in `"'23' written on left blue gate"`) |

## `nuscenes/captioning/captioning.json` — Multi-Text Captioning

Generate a caption that jointly describes the objects in the image and the
visible text on them. One row per image, 259 total. Captions are 20-100
words, generated by grounding on the same verified object/text annotations
as the other tasks, then manually spot-checked against the source images
for accuracy.

| field | type | description |
|---|---|---|
| `id` | string | unique sample id, e.g. `nuscenes_caption_00000` |
| `source` | string | `"nuscenes"` |
| `image` | string | relative path to the clean image |
| `caption` | string | a 20-100 word caption describing the scene and weaving in the visible scene-text |

## `nuscenes/text_object_grounding/text_object_grounding.json` — Text–Object Grounding

Given a text string, find all objects in the image that contain or are
associated with that text. One row per `(image, query_text)` pair; if the
same text is shared by multiple objects, all of them are listed (and all
highlighted in `image_bbox`).

| field | type | description |
|---|---|---|
| `id` | string | unique sample id, e.g. `nuscenes_tog_00000` |
| `source` | string | `"nuscenes"` |
| `image` | string | relative path to the clean image |
| `image_bbox` | string | relative path to the image with the query text box(es) (cyan) and every linked object box (red) drawn |
| `query_text` | string | the text string being grounded |
| `categories` | `[string, ...]` | category of each object this text is linked to |
| `obj_boxes` | `[[x1,y1,x2,y2], ...]` | box for each entry in `categories`, same order |

## `nuscenes/text_region_grounding/text_region_grounding.json` — Text–Region Grounding

Given a query text string, localize the corresponding text region(s) in
the image. One row per `(image, query_text)` pair; if the same text
appears multiple times in the image, every occurrence is listed (and all
highlighted in `image_bbox`).

| field | type | description |
|---|---|---|
| `id` | string | unique sample id, e.g. `nuscenes_trg_00000` |
| `source` | string | `"nuscenes"` |
| `image` | string | relative path to the clean image |
| `image_bbox` | string | relative path to the image with every occurrence of the query text boxed (cyan) |
| `query_text` | string | the text string being localized |
| `boxes` | `[[x1,y1,x2,y2], ...]` | every box where `query_text` appears in this image |

## `nuscenes/ocr/ocr.json` — OCR

Detect and recognize all visible text regions in the image. One row per
image; `image_bbox` shows every verified text box in that image.

| field | type | description |
|---|---|---|
| `id` | string | unique sample id, e.g. `nuscenes_ocr_00000` |
| `source` | string | `"nuscenes"` |
| `image` | string | relative path to the clean image |
| `image_bbox` | string | relative path to the image with all text boxes drawn |
| `texts` | `[string, ...]` | transcription of each text region in the image |
| `boxes` | `[[x1,y1,x2,y2], ...]` | box for each entry in `texts`, same order |