Datasets:
Modalities:
Image
Formats:
imagefolder
Size:
1K<n<10K
| tags: | |
| - text-recognition | |
| - dataset | |
| - text-detection | |
| - scene-text | |
| - scene-text-recognition | |
| - scene-text-detection | |
| - text-detection-recognition | |
| - icdar | |
| - total-text | |
| - curve-text | |
| task_categories: | |
| - text-retrieval | |
| - text-classification | |
| language: | |
| - en | |
| - zh | |
| size_categories: | |
| - 10K<n<100K | |
| # TextOCR Dataset | |
| ## Version 0.1 | |
| ### Training Set | |
| - **Word Annotations:** 714,770 (272MB) | |
| - **Images:** 21,778 (6.6GB) | |
| ### Validation Set | |
| - **Word Annotations:** 107,802 (39MB) | |
| - **Images:** 3,124 | |
| ### Test Set | |
| - **Metadata:** 1MB | |
| - **Images:** 3,232 (926MB) | |
| ## General Information | |
| - **License:** Data is available under CC BY 4.0 license. | |
| - **Important Note:** Numbers in the papers should be reported on the v0.1 test set. | |
| ## Images | |
| - Training and validation set images are sourced from the OpenImages train set, while test set images come from the OpenImages test set. | |
| - Validation set's images are contained in the zip for the training set's images. | |
| - **Note:** Some images in OpenImages are rotated; please check the Rotation field in the Image IDs files for train and test. | |
| ## Dataset Format | |
| The JSON format mostly follows COCO-Text v2, except the "mask" field in "anns" is named as "points" for the polygon annotation. | |
| ### Details | |
| - **Points:** A list of 2D coordinates like `[x1, y1, x2, y2, ...]`. Note that (x1, y1) is always the top-left corner of the text (in its own orientation), and the order of the points is clockwise. | |
| - **BBox:** Contains a horizontal box converted from "points" for convenience, and "area" is computed based on the width and height of the "bbox". | |
| - **Annotation:** In cases when the text is illegible or not in English, the polygon is annotated normally but the word will be annotated as a single "." symbol. Annotations are case-sensitive and can include punctuation. | |
| ## Annotation Details | |
| - Annotators were instructed to draw exactly 4 points (quadrilaterals) whenever possible, and only draw more than 4 points when necessary (for cases like curved text). | |
| ## Relationship with TextVQA/TextCaps | |
| - The image IDs in TextOCR match the IDs in TextVQA. | |
| - The train/val/test splits are the same as TextVQA/TextCaps. However, due to privacy reasons, we removed 274 images from TextVQA while creating TextOCR. | |
| ## TextOCR JSON Files Example | |
| ```json | |
| { | |
| "imgs": { | |
| "OpenImages_ImageID_1": { | |
| "id": "OpenImages_ImageID_1", | |
| "width": "INT, Width of the image", | |
| "height": "INT, Height of the image", | |
| "set": "Split train|val|test", | |
| "filename": "train|test/OpenImages_ImageID_1.jpg" | |
| }, | |
| "OpenImages_ImageID_2": { | |
| "..." | |
| } | |
| }, | |
| "anns": { | |
| "OpenImages_ImageID_1_1": { | |
| "id": "STR, OpenImages_ImageID_1_1, Specifies the nth annotation for an image", | |
| "image_id": "OpenImages_ImageID_1", | |
| "bbox": [ | |
| "FLOAT x1", | |
| "FLOAT y1", | |
| "FLOAT x2", | |
| "FLOAT y2" | |
| ], | |
| "points": [ | |
| "FLOAT x1", | |
| "FLOAT y1", | |
| "FLOAT x2", | |
| "FLOAT y2", | |
| "...", | |
| "FLOAT xN", | |
| "FLOAT yN" | |
| ], | |
| "utf8_string": "text for this annotation", | |
| "area": "FLOAT, area of this box" | |
| }, | |
| "OpenImages_ImageID_1_2": { | |
| "..." | |
| } | |
| }, | |
| "img2Anns": { | |
| "OpenImages_ImageID_1": [ | |
| "OpenImages_ImageID_1_1", | |
| "OpenImages_ImageID_1_2", | |
| "OpenImages_ImageID_1_2" | |
| ], | |
| "OpenImages_ImageID_N": [ | |
| "..." | |
| ] | |
| } | |
| } | |