doctr.datasets

doctr.datasets

class doctr.datasets.FUNSD(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any)

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

• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.SROIE(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any)

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

• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.CORD(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any)

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

• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.IIIT5K(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any)

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

• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.SVT(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any)

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

• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.SVHN(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any)

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

• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.SynthText(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any)

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

• **kwargs – keyword arguments from VisionDataset.

class doctr.datasets.IC03(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, **kwargs: Any)

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

• **kwargs – keyword arguments from VisionDataset.

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

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

• **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, **kwargs: Any)

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

• **kwargs – keyword arguments from AbstractDataset.

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

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.DocArtefacts(train: bool = True, use_polygons: bool = False, **kwargs: Any)

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.

Synthetic dataset generator

class doctr.datasets.CharacterGenerator(*args, **kwargs)

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)

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)

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)

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 – pathe 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)

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.

Dataloader

class doctr.datasets.loader.DataLoader(dataset, shuffle: bool = True, batch_size: int = 1, drop_last: bool = False, num_workers: int | None = None, collate_fn: Callable | None = None)

Implements a dataset wrapper for fast data loading

>>> from doctr.datasets import CORD, DataLoader
>>> train_set = CORD(train=True, download=True)
>>> train_loader = DataLoader(train_set, batch_size=32)
>>> train_iter = iter(train_loader)
>>> images, targets = next(train_iter)

Parameters:

• dataset – the dataset

• shuffle – whether the samples should be shuffled before passing it to the iterator

• batch_size – number of elements in each batch

• drop_last – if True, drops the last batch if it isn’t full

• num_workers – number of workers to use for data loading

• collate_fn – function to merge samples into a batch

Supported Vocabs

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

docTR Vocabs

Name	size	characters
digits	10	0123456789
hindi_digits	10	٠١٢٣٤٥٦٧٨٩
ascii_letters	52	abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ
punctuation	32	!”#$%&'()*+,-./:;<=>?@[\]^_`{|}~
currency	5	£€¥¢฿
ancient_greek	48	αβγδεζηθικλμνξοπρστυφχψωΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ
arabic_letters	37	ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىي
persian_letters	5	پچڢڤگ
arabic_diacritics	2	‘ًٌٍَُِّْ’
arabic_punctuation	5	؟؛«»—
latin	94	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~
english	100	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿
legacy_french	123	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&'()*+,-./:;<=>?@[\]^_`{|}~°àâéèêëîïôùûçÀÂÉÈËÎÏÔÙÛÇ£€¥¢฿
french	126	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿àâéèêëîïôùûüçÀÂÉÈÊËÎÏÔÙÛÜÇ
portuguese	131	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áàâãéêëíïóôõúüçÁÀÂÃÉËÍÏÓÔÕÚÜÇ¡¿
spanish	116	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áéíóúüñÁÉÍÓÚÜÑ¡¿
german	108	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿äöüßÄÖÜẞ
arabic	101	ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيپچڢڤگ؟؛«»—0123456789٠١٢٣٤٥٦٧٨٩’ًٌٍَُِّْ’!”#$%&'()*+,-./:;<=>?@[\]^_`{|}~
czech	130	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áčďéěíňóřšťúůýžÁČĎÉĚÍŇÓŘŠŤÚŮÝŽ
vietnamese	234	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áàảạãăắằẳẵặâấầẩẫậéèẻẽẹêếềểễệóòỏõọôốồổộỗơớờởợỡúùủũụưứừửữựiíìỉĩịýỳỷỹỵÁÀẢẠÃĂẮẰẲẴẶÂẤẦẨẪẬÉÈẺẼẸÊẾỀỂỄỆÓÒỎÕỌÔỐỒỔỘỖƠỚỜỞỢỠÚÙỦŨỤƯỨỪỬỮỰIÍÌỈĨỊÝỲỶỸỴ

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, **kwargs: Any) → ndarray

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