doctr.datasets

doctr.datasets

class doctr.datasets.FUNSD(train: bool = True, use_polygons: bool = False, recognition_task: bool = False, detection_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

• 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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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 – 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)

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

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]

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

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

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]]

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]

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]

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

Name	size	characters
latin	94	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~
english	100	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿
albanian	104	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿çëÇË
afrikaans	114	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿èëïîôûêÈËÏÎÔÛÊ
azerbaijani	111	0123456789abcdefghijklmnopqrstuvxyzABCDEFGHIJKLMNOPQRSTUVXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿çəğöşüÇƏĞÖŞÜ₼
basque	104	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ñçÑÇ
bosnian	102	0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿čćđšžČĆĐŠŽ
catalan	120	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿àèéíïòóúüçÀÈÉÍÏÒÓÚÜÇ
croatian	110	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ČčĆćĐ𩹮ž
czech	130	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áčďéěíňóřšťúůýžÁČĎÉĚÍŇÓŘŠŤÚŮÝŽ
danish	106	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿æøåÆØÅ
dutch	114	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áéíóúüñÁÉÍÓÚÜÑ
estonian	112	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿šžõäöüŠŽÕÄÖÜ
esperanto	105	0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ĉĝĥĵŝŭĈĜĤĴŜŬ₷
french	126	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿àâéèêëîïôùûüçÀÂÉÈÊËÎÏÔÙÛÜÇ
finnish	104	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿äöÄÖ
frisian	108	0123456789abcdefghijklmnoprstuvwyzABCDEFGHIJKLMNOPRSTUVWYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿âêôûúÂÊÔÛÚƒƑ
galician	98	0123456789abcdefghilmnopqrstuvxyzABCDEFGHILMNOPQRSTUVXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ñÑçÇ
german	108	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿äöüßÄÖÜẞ
hausa	101	0123456789abcdefghijklmnorstuwyzABCDEFGHIJKLMNORSTUWYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ɓɗƙƴƁƊƘƳ₦
hungarian	114	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áéíóöúüÁÉÍÓÖÚÜ
icelandic	114	0123456789abdefghijklmnoprstuvxyzABDEFGHIJKLMNOPRSTUVXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ðáéíóúýþæöÐÁÉÍÓÚÝÞÆÖ
indonesian	100	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿
irish	110	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áéíóúÁÉÍÓÚ
italian	120	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿àèéìíîòóùúÀÈÉÌÍÎÒÓÙÚ
latvian	116	0123456789abcdefghijklmnoprstuvyzABCDEFGHIJKLMNOPRSTUVYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿āčēģīķļņšūžĀČĒĢĪĶĻŅŠŪŽ
lithuanian	112	0123456789abcdefghijklmnoprstuvyzABCDEFGHIJKLMNOPRSTUVYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ąčęėįšųūžĄČĘĖĮŠŲŪŽ
luxembourgish	110	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿äöüéëÄÖÜÉË
malagasy	94	0123456789abdefghijklmnoprstvyzABDEFGHIJKLMNOPRSTVYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ôñÔÑ
malay	100	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿
maltese	104	0123456789abdefghijklmnopqrstuvwxzABDEFGHIJKLMNOPQRSTUVWXZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ċġħżĊĠĦŻ
maori	84	0123456789aeghikmnprtuwAEGHIKMNPRTUW!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿āēīōūĀĒĪŌŪ
montenegrin	103	0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿čćšžźČĆŠŚŽŹ
norwegian	106	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿æøåÆØÅ
polish	118	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ąćęłńóśźżĄĆĘŁŃÓŚŹŻ
portuguese	128	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áàâãéêíïóôõúüçÁÀÂÃÉÊÍÏÓÔÕÚÜÇ
quechua	90	0123456789acehiklmnopqrstuwyACEHIKLMNOPQRSTUWY!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ñÑĉĈçÇ
romanian	110	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ăâîșțĂÂÎȘȚ
scottish_gaelic	94	0123456789abcdefghilmnoprstuABCDEFGHILMNOPRSTU!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿àèìòùÀÈÌÒÙ
serbian_latin	110	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿čćđžšČĆĐŽŠ
slovak	134	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ôäčďľňšťžáéíĺóŕúýÔÄČĎĽŇŠŤŽÁÉÍĹÓŔÚÝ
slovene	102	0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿čćđšžČĆĐŠŽ
somali	94	0123456789abcdefghijklmnoqrstuwxyABCDEFGHIJKLMNOQRSTUWXY!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿
spanish	116	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áéíóúüñÁÉÍÓÚÜÑ¡¿
swahili	96	0123456789abcdefghijklmnoprstuvwyzABCDEFGHIJKLMNOPRSTUVWYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿
swedish	106	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿åäöÅÄÖ
tagalog	95	0123456789abdefghijklmnoprstuvyzABDEFGHIJKLMNOPRSTUVYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ñÑ₱
turkish	113	0123456789abcdefghijklmnoprstuvyzABCDEFGHIJKLMNOPRSTUVYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿çğıöşüâîûÇĞİÖŞÜÂÎÛ₺
uzbek_latin	110	0123456789abcdefghijklmnopqrstuvxyzABCDEFGHIJKLMNOPQRSTUVXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿çğɉñöşÇĞɈÑÖŞ
vietnamese	235	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áàảạãăắằẳẵặâấầẩẫậđéèẻẽẹêếềểễệóòỏõọôốồổộỗơớờởợỡúùủũụưứừửữựíìỉĩịýỳỷỹỵÁÀẢẠÃĂẮẰẲẴẶÂẤẦẨẪẬĐÉÈẺẼẸÊẾỀỂỄỆÓÒỎÕỌÔỐỒỔỘỖƠỚỜỞỢỠÚÙỦŨỤƯỨỪỬỮỰÍÌỈĨỊÝỲỶỸỴ₫
welsh	102	0123456789abcdefghijlmnoprstuwyABCDEFGHIJLMNOPRSTUWY!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿âêîôŵŷÂÊÎÔŴŶ
yoruba	97	0123456789abdefghijklmnoprstuwyABDEFGHIJKLMNOPRSTUWY!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ẹọṣẸỌṢ₦
zulu	100	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿
russian	114	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿₽
belarusian	116	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ўiЎI₽
ukrainian	114	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ґіїєҐІЇЄ₴
tatar	124	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿₽ӘәҖҗҢңӨөҮү
tajik	125	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ҒғҚқҲҳҶҷӢӣӮӯ
kazakh	132	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ӘәҒғҚқҢңӨөҰұҮүҺһІі₸
kyrgyz	119	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ҢңӨөҮү
bulgarian	107	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿
macedonian	119	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ЃѓЅѕЈјЉљЊњЌќЏџ
mongolian	128	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ӨөҮү᠐᠑᠒᠓᠔᠕᠖᠗᠘᠙₮
yakut	124	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ҔҕҤҥӨөҺһҮү₽
serbian_cyrillic	107	абвгдежзиклмнопрстуфхцчшАБВГДЕЖЗИКЛМНОПРСТУФХЦЧШJjЂђЉљЊњЋћЏџ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿
uzbek_cyrillic	121	абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ЎўҚқҒғҲҳ
greek	106	!”#$%&’()*+,-./:;<=>?@[]^_`{|}~αβγδεζηθικλμνξοπρστςυφχψωΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ£€¥¢฿άέήίϊΐόύϋΰώΆΈΉΊΪΌΎΫΏ
greek_extended	301	!”#$%&’()*+,-./:;<=>?@[]^_`{|}~αβγδεζηθικλμνξοπρστςυφχψωΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ£€¥¢฿άέήίϊΐόύϋΰώΆΈΉΊΪΌΎΫΏͶͷϜϝἀἁἂἃἄἅἆἇἈἉἊἋἌἍἎἏἐἑἒἓἔἕἘἙἚἛἜἝἠἡἢἣἤἥἦἧἨἩἪἫἬἭἮἯἰἱἲἳἴἵἶἷἸἹἺἻἼἽἾἿὀὁὂὃὄὅὈὉὊὋὌὍὐὑὒὓὔὕὖὗὙὛὝὟὠὡὢὣὤὥὦὧὨὩὪὫὬὭὮὯὰὲὴὶὸὺὼᾀᾁᾂᾃᾄᾅᾆᾇᾈᾉᾊᾋᾌᾍᾎᾏᾐᾑᾒᾓᾔᾕᾖᾗᾘᾙᾚᾛᾜᾝᾞᾟᾠᾡᾢᾣᾤᾥᾦᾧᾨᾩᾪᾫᾬᾭᾮᾯᾲᾳᾴᾶᾷᾺᾼῂῃῄῆῇῈῊῌῒΐῖῗῚῢΰῤῥῦῧῪῬῲῳῴῶῷῸῺῼ
hebrew	176	0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~אבגדהוזחטיךכלםמןנסעףפץצקרשתְֱֲֳִֵֶַָׇֹֺֻֽ־ֿ׀ׁׂ׃ׅׄ׆׳״֑֖֛֢֣֤֥֦֧֪֚֭֮֒֓֔֕֗֘֙֜֝֞֟֠֡֨֩֫֬֯ׯװױײיִﬞײַﬠﬡﬢﬣﬤﬥﬦﬧﬨ﬩שׁשׂשּׁשּׂאַאָאּבּגּדּהּוּזּטּיּךּכּלּמּנּסּףּפּצּקּרּשּתּוֹבֿכֿפֿﭏ₪
arabic	116	0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!”#$%&’()*+,-./:;<=>?@[]^_`{|}~
persian	116	0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!”#$%&’()*+,-./:;<=>?@[]^_`{|}~
urdu	124	0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ٹڈڑںھےہۃ
pashto	126	0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ټډړږښځڅڼېۍ
kurdish	121	0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ڵڕۆێە
uyghur	123	0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ەېۆۇۈڭھ
sindhi	133	0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ڀٿٺٽڦڄڃڇڏڌڊڍڙڳڱڻھ
devanagari	151	कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~₹
hindi	151	कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~₹
sanskrit	151	कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~₹
marathi	151	कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~₹
nepali	151	कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~₹
gujarati	121	કખગઘઙચછજઝઞટઠડઢણતથદધનપફબભમયરલળવશષસહઅઆઇઈઉઊઋઌઍએઐઑઓઔ૦૧૨૩૪૫૬૭૮૯ઁંઃ઼ાિીુૂૃૄૅેૈૉોૌૢૣૺૻૼ૽૾૿્ઽ॥!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ૐ૰૱
bengali	116	কখগঘঙচছজঝঞটঠডঢণতথদধনপফবভমযরলশষসহড়ঢ়য়ৰৱৼঅআইঈউঊঋঌএঐওঔৠৡ০১২৩৪৫৬৭৮৯ািীুূৃেৈোৌৗ্ঽৎ৽৺৻!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ঁংঃ়৳
tamil	98	கஙசஞடணதநபமயரலவழளறனஅஆஇஈஉஊஎஏஐஒஓஔ௦௧௨௩௪௫௬௭௮௯ாிீுூெேைொோௌ்௰௱௲!”#$%&’()*+,-./:;<=>?@[]^_`{|}~௳௴௵௶௷௸௹௺ஃௐ₹
telugu	119	కఖగఘఙచఛజఝఞటఠడఢణతథదధనపఫబభమయరఱలళవశషసహఴఅఆఇఈఉఊఋఌఎఏఐఒఓఔౠౡ౦౧౨౩౪౫౬౭౮౯౸౹౺౻ాిీుూృౄెేైొోౌౢౣ్ఽ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ఁంః₹
kannada	114	ಕಖಗಘಙಚಛಜಝಞಟಠಡಢಣತಥದಧನಪಫಬಭಮಯರಲವಶಷಸಹಳಅಆಇಈಉಊಋॠಌೡಎಏಐಒಓಔ೦೧೨೩೪೫೬೭೮೯ಾಿೀುೂೃೄೆೇೈೊೋೌ್।॥ೱೲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ಂಃಁ₹
sinhala	113	කඛගඝඞචඡජඣඤටඨඩඪණතථදධනපඵබභමයරලවශෂසහළෆඅආඇඈඉඊඋඌඍඎඏඐඑඒඓඔඕඖ෦෧෨෩෪෫෬෭෮෯ාැෑිීුූෙේෛොෝෞ්෴!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ංඃ₹
malayalam	116	കഖഗഘങചഛജഝഞടഠഡഢണതഥദധനപഫബഭമയരറലളഴവശഷസഹഅആഇഈഉഊഋൠഌൡഎഏഐഒഓഔ൦൧൨൩൪൫൬൭൮൯ാിീുൂൃൄൢൣെേൈൊോൌ്!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ഃ൹ഽ൏ം₹
punjabi	112	ਕਖਗਘਙਚਛਜਝਞਟਠਡਢਣਤਥਦਧਨਪਫਬਭਮਯਰਲਵਸ਼ਸਹਖ਼ਗ਼ਜ਼ਫ਼ੜਲ਼ਅਆਇਈਉਊਏਐਓਔੲੳ੦੧੨੩੪੫੬੭੮੯ਂ਼ਾਿੀੁੂੇੈੋੌੑੰੱੵ੍।॥!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ੴ₹
odia	121	କଖଗଘଙଚଛଜଝଞଟଠଡଢଣତଥଦଧନପଫବଭମଯରଲଳଵଶଷସହୟୱଡ଼ଢ଼ଅଆଇଈଉଊଋଌଏଐଓଔୡୠ୦୧୨୩୪୫୬୭୮୯୲୳୴୵୶୷ାିୀୁୂୃୄେୈୋୌୢୣ୍ଽ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ଂଃଁ଼୰₹
khmer	134	កខគឃងចឆជឈញដឋឌឍណតថទធនបផពភមយរលវឝឞសហឡអឣឤឥឦឧឨឩឪឫឬឭឮឯឰឱឲឳ០១២៣៤៥៦៧៨៩ាិីឹឺុូួើឿៀេែៃោៅ្ំះៈ៉៊់៌៍៎៏័៑៓៝។៕៖៘៙៚ៗៜ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~៛
armenian	131	ԱԲԳԴԵԶԷԸԹԺԻԼԽԾԿՀՁՂՃՄՅՆՇՈՉՊՋՌՍՎՏՐՑՒՓՔՕՖՙՠաբգդեզէըթժիլխծկհձղճմյնշոչպջռսվտրցւփքօֆևֈ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~՚՛՜՝՞՟։֊֏
sudanese	106	0123456789᮰᮱᮲᮳᮴᮵᮶᮷᮸᮹ᮊᮋᮌᮍᮎᮏᮐᮑᮒᮓᮔᮕᮖᮗᮘᮙᮚᮛᮜᮝᮞᮟᮠᮮᮯᮺᮻᮼᮽᮾᮿᮃᮄᮅᮆᮇᮈᮉᮀᮁᮂᮡᮢᮣᮤᮥᮦᮧᮨᮩ᮪᮫ᮬᮭ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~
thai	129	0123456789๐๑๒๓๔๕๖๗๘๙!”#$%&’()*+,-./:;<=>?@[]^_`{|}~๏๚๛ๆฯกขฃคฅฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลฦวศษสหฬอฮะาำเแโใไๅัิีึืฺุู็่้๊๋์ํ๎฿
lao	124	0123456789໐໑໒໓໔໕໖໗໘໙!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ໆໞໟຯກຂຄຆງຈຉຊຌຍຎຏຐຑຒຓດຕຖທຘນບປຜຝພຟຠມຢຣລວຨຩສຫຬອຮະາຳຽເແໂໃໄໜໝັິີຶື຺ຸູົຼ່້໊໋໌ໍ
burmese	130	0123456789၀၁၂၃၄၅၆၇၈၉႐႑႒႓႔႕႖႗႘႙ကခဂဃငစဆဇဈဉညဋဌဍဎဏတထဒဓနပဖဗဘမယရလဝသဟဠအၐၑၒၓၔၕၚၛၜၝၡၥၦၮၯၰၵၶၷၸၹၺၻၼၽၾၿႀႁႎဣဤဥဦဧဩဪဿ့းံါာိီုူေဲဳဴဵျြွှ္်၊။၌၍၎၏ၤၗ
javanese	124	0123456789꧐꧑꧒꧓꧔꧕꧖꧗꧘꧙ꦏꦐꦑꦒꦓꦔꦕꦖꦗꦘꦙꦚꦛꦜꦝꦞꦟꦠꦡꦢꦣꦤꦥꦦꦧꦨꦩꦪꦫꦬꦭꦮꦯꦰꦱꦲꦄꦅꦆꦇꦈꦉꦊꦋꦌꦍꦎꦴꦵꦶꦷꦸꦹꦺꦻꦼꦀꦁꦂꦃ꦳ꦽꦾꦿ꧀꧈꧉꧊꧋꧌꧍ꧏ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~
georgian	131	0123456789ႠႡႢႣႤႥႦႧႨႩႪႫႬႭႮႯႰႱႲႳႴႵႶႷႸႹႺႻႼႽႾႿჀჁჂჃჄჅჇჍაბგდევზთიკლმნოპჟრსტუფქღყშჩცძწჭხჯჰჱჲჳჴჵჶჷჸჹჺჼჽჾჿ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~჻₾
ethiopic	362	ሀሁሂሃሄህሆሇለሉሊላሌልሎሏሐሑሒሓሔሕሖሗመሙሚማሜምሞሟሠሡሢሣሤሥሦሧረሩሪራሬርሮሯሰሱሲሳሴስሶሷሸሹሺሻሼሽሾሿቀቁቂቃቄቅቆቇቈቊቋቌቍቐቑቒቓቔቕቖቘቚቛቜቝበቡቢባቤብቦቧቨቩቪቫቬቭቮቯተቱቲታቴትቶቷቸቹቺቻቼችቾቿኀኁኂኃኄኅኆኇኈኊኋኌኍነኑኒናኔንኖኗኘኙኚኛኜኝኞኟአኡኢኣኤእኦኧከኩኪካኬክኮኯኰኲኳኴኵኸኹኺኻኼኽኾዀዂዃዄዅወዉዊዋዌውዎዏዐዑዒዓዔዕዖዘዙዚዛዜዝዞዟዠዡዢዣዤዥዦዧየዩዪያዬይዮዯደዱዲዳዴድዶዷዸዹዺዻዼዽዾዿጀጁጂጃጄጅጆጇገጉጊጋጌግጎጏጐጒጓጔጕጘጙጚጛጜጝጞጟጠጡጢጣጤጥጦጧጨጩጪጫጬጭጮጯጰጱጲጳጴጵጶጷጸጹጺጻጼጽጾጿፀፁፂፃፄፅፆፇፈፉፊፋፌፍፎፏፐፑፒፓፔፕፖፗፘፙፚᎀᎁᎂᎃᎄᎅᎆᎇᎈᎉᎊᎋᎌᎍᎎᎏ፩፪፫፬፭፮፯፰፱፲፳፴፵፶፷፸፹፺፻፼
japanese	2383	0123456789ぁあぃいぅうぇえぉおかがきぎく…路露老労弄郎朗浪廊楼漏籠六録麓論和話賄脇惑枠湾腕!”#$%&’()*+,-./:;<=>?@[]^_`{|}~。・〜°—、「」『』【】゛》《〉〈£€¥¢฿
korean	11237	0123456789가각갂갃간갅갆갇갈갉갊틹틺틻틼틽틾틿팀폨폩…흿힀힁힂힃힄힅힆힇히힉힊힋힌힍힎힏힐힑힒힓힔힕힖힗힘힙힚힛힜힝힞힟힠힡힢힣!”#$%&’()*+,-./:;<=>?@[]^_`{|}~。・〜°—、「」『』【】゛》《〉〈£€¥¢฿₩
simplified_chinese	6656	0123456789㐀㐁㐂㐃㐄㐅㐆㐇㐈㐉㐊㐋㐌㐍㐎㐏㐐㐑㐒㐓㐔㐕㐖㐗㐘㐙㐚…䶮䶯䶰䶱䶲䶳䶴䶵䶶䶷䶸䶹䶺䶻䶼䶽䶾䶿!”#$%&’()*+,-./:;<=>?@[]^_`{|}~。・〜°—、「」『』【】゛》《〉〈£€¥¢฿
multilingual	726	0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿çëÇËèïîôûêÈÏÎÔÛÊəğöşüƏĞÖŞÜ₼ñÑčćđšžČĆĐŠŽàéíòóúÀÉÍÒÓÚáďěňřťůýÁĎĚŇŘŤŮÝæøåÆØÅõäÕÄĉĝĥĵŝŭĈĜĤĴŜŬ₷âùÂÙƒƑßẞɓɗƙƴƁƊƘƳ₦ðþÐÞìÌāēģīķļņūĀĒĢĪĶĻŅŪąęėįųĄĘĖĮŲōŌċġħżĊĠĦŻźŚŹłńśŁŃãÃășțĂȘȚľĺŕĽĹŔ¡¿₱ıİ₺ɉɈảạắằẳẵặấầẩẫậẻẽẹếềểễệỏọốồổộỗơớờởợỡủũụưứừửữựỉĩịỳỷỹỵẢẠẮẰẲẴẶẤẦẨẪẬẺẼẸẾỀỂỄỆỎỌỐỒỔỘỖƠỚỜỞỢỠỦŨỤƯỨỪỬỮỰỈĨỊỲỶỸỴ₫ŵŷŴŶṣṢ§абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ₽ўЎґіїєҐІЇЄ₴ӘәҖҗҢңӨөҮүҒғҚқҲҳҶҷӢӣӮӯҰұҺһ₸ЃѓЅѕЈјЉљЊњЌќЏџ᠐᠑᠒᠓᠔᠕᠖᠗᠘᠙₮ҔҕҤҥЂђЋћαβγδεζηθικλμνξοπρστςυφχψωΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩάέήίϊΐόύϋΰώΆΈΉΊΪΌΎΫΏאבגדהוזחטיךכלםמןנסעףפץצקרשתְֱֲֳִֵֶַָׇֹֺֻֽ־ֿ׀ׁׂ׃ׅׄ׆׳״֑֖֛֢֣֤֥֦֧֪֚֭֮֒֓֔֕֗֘֙֜֝֞֟֠֡֨֩֫֬֯ׯװױײיִﬞײַﬠﬡﬢﬣﬤﬥﬦﬧﬨ﬩שׁשׂשּׁשּׂאַאָאּבּגּדּהּוּזּטּיּךּכּלּמּנּסּףּפּצּקּרּשּתּוֹבֿכֿפֿﭏ₪