| --- |
| language: |
| - en |
| license: |
| - mit |
| source_datasets: |
| - original |
| task_categories: |
| - image-segmentation |
| - object-detection |
| task_ids: [] |
| tags: |
| - optical-character-recognition |
| - text-detection |
| - ocr |
| --- |
| |
|
|
| # School Notebooks Dataset |
|
|
| The images of school notebooks with handwritten notes in English. |
|
|
| The dataset annotation contain end-to-end markup for training detection and OCR models, as well as an end-to-end model for reading text from pages. |
|
|
| ## Annotation format |
|
|
| The annotation is in COCO format. The `annotation.json` should have the following dictionaries: |
|
|
| - `annotation["categories"]` - a list of dicts with a categories info (categotiy names and indexes). |
| - `annotation["images"]` - a list of dictionaries with a description of images, each dictionary must contain fields: |
| - `file_name` - name of the image file. |
| - `id` for image id. |
| - `annotation["annotations"]` - a list of dictioraties with a murkup information. Each dictionary stores a description for one polygon from the dataset, and must contain the following fields: |
| - `image_id` - the index of the image on which the polygon is located. |
| - `category_id` - the polygon’s category index. |
| - `attributes` - dict with some additional annotation information. In the `translation` subdict you can find text translation for the line. |
| - `segmentation` - the coordinates of the polygon, a list of numbers - which are coordinate pairs x and y. |
