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Create README.md
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README.md
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---
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tags:
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- text-recognition
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- dataset
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- text-detection
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- scene-text
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- scene-text-recognition
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- scene-text-detection
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- text-detection-recognition
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- icdar
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- total-text
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- curve-text
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task_categories:
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- text-retrieval
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- text-classification
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language:
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- en
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- zh
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size_categories:
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- 10K<n<100K
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---
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# TextOCR Dataset
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## Version 0.1
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### Training Set
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- **Word Annotations:** 714,770 (272MB)
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- **Images:** 21,778 (6.6GB)
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### Validation Set
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- **Word Annotations:** 107,802 (39MB)
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- **Images:** 3,124
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### Test Set
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- **Metadata:** 1MB
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- **Images:** 3,232 (926MB)
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## General Information
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- **License:** Data is available under CC BY 4.0 license.
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- **Important Note:** Numbers in the papers should be reported on the v0.1 test set.
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## Images
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- Training and validation set images are sourced from the OpenImages train set, while test set images come from the OpenImages test set.
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- Validation set's images are contained in the zip for the training set's images.
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- **Note:** Some images in OpenImages are rotated; please check the Rotation field in the Image IDs files for train and test.
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## Dataset Format
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The JSON format mostly follows COCO-Text v2, except the "mask" field in "anns" is named as "points" for the polygon annotation.
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### Details
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- **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.
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- **BBox:** Contains a horizontal box converted from "points" for convenience, and "area" is computed based on the width and height of the "bbox".
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- **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.
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## Annotation Details
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- 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).
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## Relationship with TextVQA/TextCaps
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- The image IDs in TextOCR match the IDs in TextVQA.
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- 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.
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## TextOCR JSON Files Example
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```json
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{
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"imgs": {
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"OpenImages_ImageID_1": {
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"id": "OpenImages_ImageID_1",
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"width": "INT, Width of the image",
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"height": "INT, Height of the image",
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"set": "Split train|val|test",
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"filename": "train|test/OpenImages_ImageID_1.jpg"
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},
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"OpenImages_ImageID_2": {
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"..."
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}
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},
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"anns": {
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"OpenImages_ImageID_1_1": {
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"id": "STR, OpenImages_ImageID_1_1, Specifies the nth annotation for an image",
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"image_id": "OpenImages_ImageID_1",
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"bbox": [
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"FLOAT x1",
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"FLOAT y1",
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"FLOAT x2",
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"FLOAT y2"
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],
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"points": [
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"FLOAT x1",
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"FLOAT y1",
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"FLOAT x2",
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"FLOAT y2",
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"...",
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"FLOAT xN",
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"FLOAT yN"
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],
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"utf8_string": "text for this annotation",
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"area": "FLOAT, area of this box"
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},
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"OpenImages_ImageID_1_2": {
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"..."
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}
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},
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"img2Anns": {
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"OpenImages_ImageID_1": [
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"OpenImages_ImageID_1_1",
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"OpenImages_ImageID_1_2",
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"OpenImages_ImageID_1_2"
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],
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"OpenImages_ImageID_N": [
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"..."
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]
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}
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}
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