

# 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.
task_categories:image-segmentation task_categories:object-detection source_datasets:original language:en license:mit optical-character-recognition text-detection ocr region:us
{"language": ["en"], "license": ["mit"], "source_datasets": ["original"], "task_categories": ["image-segmentation", "object-detection"], "task_ids": [], "tags": ["optical-character-recognition", "text-detection", "ocr"]}
TAGS
#task_categories-image-segmentation #task_categories-object-detection #source_datasets-original #language-English #license-mit #optical-character-recognition #text-detection #ocr #region-us 



# 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 'URL' 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.
# 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 'URL' 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.
TAGS
#task_categories-image-segmentation #task_categories-object-detection #source_datasets-original #language-English #license-mit #optical-character-recognition #text-detection #ocr #region-us 
 # 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 'URL' 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.
passage: TAGS
#task_categories-image-segmentation #task_categories-object-detection #source_datasets-original #language-English #license-mit #optical-character-recognition #text-detection #ocr #region-us 
# 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 'URL' 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.