doctr.datasets - docTR documentation (original) (raw)

doctr.datasets¶

class doctr.datasets.FUNSD(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

FUNSD dataset from “FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents”.

from doctr.datasets import FUNSD train_set = FUNSD(train=True, download=True) img, target = train_set[0]

Parameters:

• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.SROIE(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

SROIE dataset from “ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction”.

from doctr.datasets import SROIE train_set = SROIE(train=True, download=True) img, target = train_set[0]

Parameters:

• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.CORD(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

CORD dataset from “CORD: A Consolidated Receipt Dataset forPost-OCR Parsing”.

from doctr.datasets import CORD train_set = CORD(train=True, download=True) img, target = train_set[0]

Parameters:

• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.IIIT5K(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

IIIT-5K character-level localization dataset from“BMVC 2012 Scene Text Recognition using Higher Order Language Priors”.

NOTE: this dataset is for character-level localization

from doctr.datasets import IIIT5K train_set = IIIT5K(train=True, download=True) img, target = train_set[0]

Parameters:

• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.SVT(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

SVT dataset from “The Street View Text Dataset - UCSD Computer Vision”.

from doctr.datasets import SVT train_set = SVT(train=True, download=True) img, target = train_set[0]

Parameters:

• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.SVHN(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

SVHN dataset from “The Street View House Numbers (SVHN) Dataset”.

from doctr.datasets import SVHN train_set = SVHN(train=True, download=True) img, target = train_set[0]

Parameters:

• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.SynthText(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

SynthText dataset from “Synthetic Data for Text Localisation in Natural Images” | “repository” |“website”.

from doctr.datasets import SynthText train_set = SynthText(train=True, download=True) img, target = train_set[0]

Parameters:

• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.IC03(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

IC03 dataset from “ICDAR 2003 Robust Reading Competitions: Entries, Results and Future Directions”.

from doctr.datasets import IC03 train_set = IC03(train=True, download=True) img, target = train_set[0]

Parameters:

• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.IC13(img_folder: str, label_folder: str, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

IC13 dataset from “ICDAR 2013 Robust Reading Competition”.

NOTE: You need to download both image and label parts from Focused Scene Text challenge Task2.1 2013-2015.

from doctr.datasets import IC13 train_set = IC13(img_folder="/path/to/Challenge2_Training_Task12_Images", label_folder="/path/to/Challenge2_Training_Task1_GT") img, target = train_set[0] test_set = IC13(img_folder="/path/to/Challenge2_Test_Task12_Images", label_folder="/path/to/Challenge2_Test_Task1_GT") img, target = test_set[0]

Parameters:

• img_folder – folder with all the images of the dataset
• label_folder – folder with all annotation files for the images
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from AbstractDataset.

class doctr.datasets.IMGUR5K(img_folder: str, label_path: str, train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

IMGUR5K dataset from “TextStyleBrush: Transfer of Text Aesthetics from a Single Example” |repository.

NOTE: You need to download/generate the dataset from the repository.

from doctr.datasets import IMGUR5K train_set = IMGUR5K(train=True, img_folder="/path/to/IMGUR5K-Handwriting-Dataset/images", label_path="/path/to/IMGUR5K-Handwriting-Dataset/dataset_info/imgur5k_annotations.json") img, target = train_set[0] test_set = IMGUR5K(train=False, img_folder="/path/to/IMGUR5K-Handwriting-Dataset/images", label_path="/path/to/IMGUR5K-Handwriting-Dataset/dataset_info/imgur5k_annotations.json") img, target = test_set[0]

Parameters:

• img_folder – folder with all the images of the dataset
• label_path – path to the annotations file of the dataset
• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from AbstractDataset.

class doctr.datasets.MJSynth(img_folder: str, label_path: str, train: bool = True, **kwargs: Any)[source]¶

MJSynth dataset from “Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition”.

NOTE: This is a pure recognition dataset without bounding box labels.

NOTE: You need to download the dataset.

from doctr.datasets import MJSynth train_set = MJSynth(img_folder="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px", label_path="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px/imlist.txt", train=True) img, target = train_set[0] test_set = MJSynth(img_folder="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px", label_path="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px/imlist.txt") train=False) img, target = test_set[0]

Parameters:

• img_folder – folder with all the images of the dataset
• label_path – path to the file with the labels
• train – whether the subset should be the training one
• **kwargs – keyword arguments from AbstractDataset.

class doctr.datasets.IIITHWS(img_folder: str, label_path: str, train: bool = True, **kwargs: Any)[source]¶

IIITHWS dataset from “Generating Synthetic Data for Text Recognition” | “repository” |“website”.

NOTE: This is a pure recognition dataset without bounding box labels.

NOTE: You need to download the dataset.

from doctr.datasets import IIITHWS train_set = IIITHWS(img_folder="/path/to/iiit-hws/Images_90K_Normalized", label_path="/path/to/IIIT-HWS-90K.txt", train=True) img, target = train_set[0] test_set = IIITHWS(img_folder="/path/to/iiit-hws/Images_90K_Normalized", label_path="/path/to/IIIT-HWS-90K.txt") train=False) img, target = test_set[0]

Parameters:

• img_folder – folder with all the images of the dataset
• label_path – path to the file with the labels
• train – whether the subset should be the training one
• **kwargs – keyword arguments from AbstractDataset.

class doctr.datasets.DocArtefacts(train: bool = True, use_polygons: bool = False, **kwargs: Any)[source]¶

Object detection dataset for non-textual elements in documents. The dataset includes a variety of synthetic document pages with non-textual elements.

from doctr.datasets import DocArtefacts train_set = DocArtefacts(train=True, download=True) img, target = train_set[0]

Parameters:

• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.WILDRECEIPT(img_folder: str, label_path: str, train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

WildReceipt dataset from “Spatial Dual-Modality Graph Reasoning for Key Information Extraction” |“repository”.

NOTE: You need to download the dataset first.

from doctr.datasets import WILDRECEIPT train_set = WILDRECEIPT(train=True, img_folder="/path/to/wildreceipt/", label_path="/path/to/wildreceipt/train.txt") img, target = train_set[0] test_set = WILDRECEIPT(train=False, img_folder="/path/to/wildreceipt/", label_path="/path/to/wildreceipt/test.txt") img, target = test_set[0]

Parameters:

• img_folder – folder with all the images of the dataset
• label_path – path to the annotations file of the dataset
• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from AbstractDataset.

class doctr.datasets.COCOTEXT(img_folder: str, label_path: str, train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_task: bool = False, **kwargs: Any)[source]¶

COCO-Text dataset from “COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images” |“homepage”.

NOTE: You need to download the dataset first.

from doctr.datasets import COCOTEXT train_set = COCOTEXT(train=True, img_folder="/path/to/coco_text/train2014/", label_path="/path/to/coco_text/cocotext.v2.json") img, target = train_set[0] test_set = COCOTEXT(train=False, img_folder="/path/to/coco_text/train2014/", label_path = "/path/to/coco_text/cocotext.v2.json") img, target = test_set[0]

Parameters:

• img_folder – folder with all the images of the dataset
• label_path – path to the annotations file of the dataset
• train – whether the subset should be the training one
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• recognition_task – whether the dataset should be used for recognition task
• detection_task – whether the dataset should be used for detection task
• **kwargs – keyword arguments from AbstractDataset.

Synthetic dataset generator¶

class doctr.datasets.CharacterGenerator(*args, **kwargs)[source]¶

Implements a character image generation dataset

from doctr.datasets import CharacterGenerator ds = CharacterGenerator(vocab='abdef', num_samples=100) img, target = ds[0]

Parameters:

• vocab – vocabulary to take the character from
• num_samples – number of samples that will be generated iterating over the dataset
• cache_samples – whether generated images should be cached firsthand
• font_family – font to use to generate the text images
• img_transforms – composable transformations that will be applied to each image
• sample_transforms – composable transformations that will be applied to both the image and the target

class doctr.datasets.WordGenerator(vocab: str, min_chars: int, max_chars: int, num_samples: int, cache_samples: bool = False, font_family: str | list[str] | None = None, img_transforms: Callable[[Any], Any] | None = None, sample_transforms: Callable[[Any, Any], tuple[Any, Any]] | None = None)[source]¶

Implements a character image generation dataset

from doctr.datasets import WordGenerator ds = WordGenerator(vocab='abdef', min_chars=1, max_chars=32, num_samples=100) img, target = ds[0]

Parameters:

• vocab – vocabulary to take the character from
• min_chars – minimum number of characters in a word
• max_chars – maximum number of characters in a word
• num_samples – number of samples that will be generated iterating over the dataset
• cache_samples – whether generated images should be cached firsthand
• font_family – font to use to generate the text images
• img_transforms – composable transformations that will be applied to each image
• sample_transforms – composable transformations that will be applied to both the image and the target

Custom dataset loader¶

class doctr.datasets.DetectionDataset(img_folder: str, label_path: str, use_polygons: bool = False, **kwargs: Any)[source]¶

Implements a text detection dataset

from doctr.datasets import DetectionDataset train_set = DetectionDataset(img_folder="/path/to/images", label_path="/path/to/labels.json") img, target = train_set[0]

Parameters:

• img_folder – folder with all the images of the dataset
• label_path – path to the annotations of each image
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• **kwargs – keyword arguments from AbstractDataset.

class doctr.datasets.RecognitionDataset(img_folder: str, labels_path: str, **kwargs: Any)[source]¶

Dataset implementation for text recognition tasks

from doctr.datasets import RecognitionDataset train_set = RecognitionDataset(img_folder="/path/to/images", labels_path="/path/to/labels.json") img, target = train_set[0]

Parameters:

• img_folder – path to the images folder
• labels_path – path to the json file containing all labels (character sequences)
• **kwargs – keyword arguments from AbstractDataset.

class doctr.datasets.OCRDataset(img_folder: str, label_file: str, use_polygons: bool = False, **kwargs: Any)[source]¶

Implements an OCR dataset

from doctr.datasets import OCRDataset train_set = OCRDataset(img_folder="/path/to/images", label_file="/path/to/labels.json") img, target = train_set[0]

Parameters:

• img_folder – local path to image folder (all jpg at the root)
• label_file – local path to the label file
• use_polygons – whether polygons should be considered as rotated bounding box (instead of straight ones)
• **kwargs – keyword arguments from AbstractDataset.

Dataset utils¶

doctr.datasets.translate(input_string: str, vocab_name: str, unknown_char: str = '■') → str[source]¶

Translate a string input in a given vocabulary

Parameters:

• input_string – input string to translate
• vocab_name – vocabulary to use (french, latin, …)
• unknown_char – unknown character for non-translatable characters

Returns:

A string translated in a given vocab

doctr.datasets.encode_string(input_string: str, vocab: str) → list[int][source]¶

Given a predefined mapping, encode the string to a sequence of numbers

Parameters:

• input_string – string to encode
• vocab – vocabulary (string), the encoding is given by the indexing of the character sequence

Returns:

A list encoding the input_string

doctr.datasets.decode_sequence(input_seq: ndarray | Sequence[int], mapping: str) → str[source]¶

Given a predefined mapping, decode the sequence of numbers to a string

Parameters:

• input_seq – array to decode
• mapping – vocabulary (string), the encoding is given by the indexing of the character sequence

Returns:

A string, decoded from input_seq

doctr.datasets.encode_sequences(sequences: list[str], vocab: str, target_size: int | None = None, eos: int = -1, sos: int | None = None, pad: int | None = None, dynamic_seq_length: bool = False) → ndarray[source]¶

Encode character sequences using a given vocab as mapping

Parameters:

• sequences – the list of character sequences of size N
• vocab – the ordered vocab to use for encoding
• target_size – maximum length of the encoded data
• eos – encoding of End Of String
• sos – optional encoding of Start Of String
• pad – optional encoding for padding. In case of padding, all sequences are followed by 1 EOS then PAD
• dynamic_seq_length – if target_size is specified, uses it as upper bound and enables dynamic sequence size

Returns:

the padded encoded data as a tensor

doctr.datasets.pre_transform_multiclass(img, target: tuple[ndarray, list]) → tuple[ndarray, dict[str, list]][source]¶

Converts multiclass target to relative coordinates.

Parameters:

• img – Image
• target – tuple of target polygons and their classes names

Returns:

Image and dictionary of boxes, with class names as keys

doctr.datasets.crop_bboxes_from_image(img_path: str | Path, geoms: ndarray) → list[ndarray][source]¶

Crop a set of bounding boxes from an image

Parameters:

• img_path – path to the image
• geoms – a array of polygons of shape (N, 4, 2) or of straight boxes of shape (N, 4)

Returns:

a list of cropped images

doctr.datasets.convert_target_to_relative(img: ImageTensor, target: ndarray | dict[str, Any]) → tuple[ImageTensor, dict[str, Any] | ndarray][source]¶

Converts target to relative coordinates

Parameters:

• img – tf.Tensor or torch.Tensor representing the image
• target – target to convert to relative coordinates (boxes (N, 4) or polygons (N, 4, 2))

Returns:

The image and the target in relative coordinates

Supported Vocabs¶

Since textual content has to be encoded properly for models to interpret them efficiently, docTR supports multiple sets of vocabs.

docTR Vocabs¶