Create README.md

README.md

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+---
+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": [
+      "..."
+    ]
+  }
+}