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)[source]

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

https://doctr-static.mindee.com/models?id=v0.5.0/funsd-grid.png&src=0 
>>> from doctr.datasets import FUNSD
>>> train_set = FUNSD(train=True, download=True)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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”.

https://doctr-static.mindee.com/models?id=v0.5.0/sroie-grid.png&src=0 
>>> from doctr.datasets import SROIE
>>> train_set = SROIE(train=True, download=True)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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”.

https://doctr-static.mindee.com/models?id=v0.5.0/cord-grid.png&src=0 
>>> from doctr.datasets import CORD
>>> train_set = CORD(train=True, download=True)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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”.

https://doctr-static.mindee.com/models?id=v0.5.0/iiit5k-grid.png&src=0 
>>> # NOTE: this dataset is for character-level localization
>>> from doctr.datasets import IIIT5K
>>> train_set = IIIT5K(train=True, download=True)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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”.

https://doctr-static.mindee.com/models?id=v0.5.0/svt-grid.png&src=0 
>>> from doctr.datasets import SVT
>>> train_set = SVT(train=True, download=True)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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”.

https://doctr-static.mindee.com/models?id=v0.5.0/svhn-grid.png&src=0 
>>> from doctr.datasets import SVHN
>>> train_set = SVHN(train=True, download=True)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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”.

https://doctr-static.mindee.com/models?id=v0.5.0/svt-grid.png&src=0 
>>> from doctr.datasets import SynthText
>>> train_set = SynthText(train=True, download=True)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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”.

https://doctr-static.mindee.com/models?id=v0.5.0/ic03-grid.png&src=0 
>>> from doctr.datasets import IC03
>>> train_set = IC03(train=True, download=True)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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”.

https://doctr-static.mindee.com/models?id=v0.5.0/ic13-grid.png&src=0 
>>> # 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")
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
>>> test_set = IC13(img_folder="/path/to/Challenge2_Test_Task12_Images",
>>>                 label_folder="/path/to/Challenge2_Test_Task1_GT")
>>> sample = test_set[0]
>>> img, target = sample.image, sample.target
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.

https://doctr-static.mindee.com/models?id=v0.5.0/imgur5k-grid.png&src=0 
>>> # 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")
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
>>> 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")
>>> sample = test_set[0]
>>> img, target = sample.image, sample.target
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)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
>>> 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)
>>> sample = test_set[0]
>>> img, target = sample.image, sample.target
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)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
>>> test_set = IIITHWS(img_folder="/path/to/iiit-hws/Images_90K_Normalized",
>>>                    label_path="/path/to/IIIT-HWS-90K.txt")
>>>                    train=False)
>>> sample = test_set[0]
>>> img, target = sample.image, sample.target
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.

https://doctr-static.mindee.com/models?id=v0.5.0/artefacts-grid.png&src=0 
>>> from doctr.datasets import DocArtefacts
>>> train_set = DocArtefacts(train=True, download=True)
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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”.

https://doctr-static.mindee.com/models?id=v0.7.0/wildreceipt-dataset.jpg&src=0 
>>> # 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")
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
>>> test_set = WILDRECEIPT(train=False, img_folder="/path/to/wildreceipt/",
>>>                    label_path="/path/to/wildreceipt/test.txt")
>>> sample = test_set[0]
>>> img, target = sample.image, sample.target
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")
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
>>> test_set = COCOTEXT(train=False, img_folder="/path/to/coco_text/train2014/",
>>> label_path = "/path/to/coco_text/cocotext.v2.json")
>>> sample = test_set[0]
>>> img, target = sample.image, sample.target
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)
>>> sample = ds[0]
>>> img, target = sample.image, sample.target
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[[Sample], Sample] | 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)
>>> sample = ds[0]
>>> img, target = sample.image, sample.target
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")
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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.LayoutDataset(img_folder: str, label_path: str, use_polygons: bool = False, **kwargs: Any)[source]

Implements a document layout detection dataset.

>>> from doctr.datasets import LayoutDataset
>>> train_set = LayoutDataset(
>>>     img_folder="/path/to/images",
>>>     label_path="/path/to/labels.json",
>>> )
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
Parameters:

  • img_folder – folder containing the dataset images

  • label_path – path to the labels.json file

  • use_polygons – whether to keep polygons instead of converting to straight boxes

  • **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")
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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.TableStructureDataset(img_folder: str, label_path: str, use_polygons: bool = False, **kwargs: Any)[source]

Table structure recognition dataset for table structure recognition.

The labels file maps each image name to its cells and their logical coordinates:

{
    "table_0.jpg": {
        "cells": [[[x0, y0], [x1, y1], [x2, y2], [x3, y3]], ...],   # quads (TL, TR, BR, BL), abs px
        "logic": [[start_col, end_col, start_row, end_row], ...]      # 0-indexed, per cell
    },
    ...
}

Each sample yields the image and a target containing relative cells and their logical coordinates. Cells have shape (N, 4) by default, or (N, 4, 2) when use_polygons=True. Logical coordinates have shape (N, 4).

>>> from doctr.datasets import TableStructureDataset
>>> from doctr.transforms import Resize
>>> train_set = TableStructureDataset(
>>>     img_folder="/path/to/images",
>>>     label_path="/path/to/labels.json",
>>> )
>>> img, target = train_set[0]
Parameters:

  • img_folder – folder with all the dataset images

  • label_path – path to the JSON labels

  • use_polygons – whether to keep cell polygons instead of converting them to straight boxes

  • **kwargs – keyword arguments from AbstractDataset (e.g. img_transforms, sample_transforms)

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")
>>> sample = train_set[0]
>>> img, target = sample.image, sample.target
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], class_names: list[str] | None = None) → tuple[ndarray, dict[str, ndarray]][source]

Converts multiclass target to relative coordinates.

Parameters:

  • img – Image

  • target – tuple of target polygons and their classes names

  • class_names – if given, the returned dictionary contains exactly these classes, in this order. Classes without any box in this sample map to an empty array. This keeps the class -> channel mapping stable across samples, which the detection models rely on.

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

Name

size

characters

latex

81

()+,-./0123456789<=>ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_abcdefghijklmnopqrstuvwxyz{|}

latin

94

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~

english

100

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿

albanian

104

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿çëÇË

afrikaans

128

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿èëïîôûêÈËÏÎÔÛÊáéíóúäöÁÉÍÓÚÄÖ

azerbaijani

113

0123456789abcdefghijklmnopqrstuvxyzABCDEFGHIJKLMNOPQRSTUVXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿çəğıöşüÇƏĞİÖŞÜ₼

basque

104

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ñçÑÇ

bosnian

102

0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿čćđšžČĆĐŠŽ

catalan

123

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿àèéíïòóúüçÀÈÉÍÏÒÓÚÜÇ·ŀĿ

croatian

110

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ČčĆćĐ𩹮ž

czech

130

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danish

106

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dutch

126

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áéíóúüñÁÉÍÓÚÜÑäëïöèêÄËÏÖÈÊ

estonian

112

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿šžõäöüŠŽÕÄÖÜ

esperanto

105

0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ĉĝĥĵŝŭĈĜĤĴŜŬ₷

french

126

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finnish

104

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿äöÄÖ

frisian

108

0123456789abcdefghijklmnoprstuvwyzABCDEFGHIJKLMNOPRSTUVWYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿âêôûúÂÊÔÛÚƒƑ

galician

110

0123456789abcdefghilmnopqrstuvxyzABCDEFGHILMNOPQRSTUVXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ñÑçÇáéíóúüÁÉÍÓÚÜ

german

108

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿äöüßÄÖÜẞ

hausa

101

0123456789abcdefghijklmnorstuwyzABCDEFGHIJKLMNORSTUWYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ɓɗƙƴƁƊƘƳ₦

hungarian

118

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿áéíóöúüÁÉÍÓÖÚÜőűŐŰ

icelandic

114

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indonesian

100

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿

irish

110

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italian

120

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latvian

116

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lithuanian

112

0123456789abcdefghijklmnoprstuvyzABCDEFGHIJKLMNOPRSTUVYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ąčęėįšųūžĄČĘĖĮŠŲŪŽ

luxembourgish

110

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿äöüéëÄÖÜÉË

malagasy

94

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malay

100

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿

maltese

114

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maori

86

0123456789aeghikmnoprtuwAEGHIKMNOPRTUW!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿āēīōūĀĒĪŌŪ

montenegrin

104

0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿čćšśžźČĆŠŚŽŹ

norwegian

106

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿æøåÆØÅ

polish

118

0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿ąćęłńóśźżĄĆĘŁŃÓŚŹŻ

portuguese

128

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quechua

90

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romanian

110

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scottish_gaelic

94

0123456789abcdefghilmnoprstuABCDEFGHILMNOPRSTU!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿àèìòùÀÈÌÒÙ

serbian_latin

110

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slovak

134

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slovene

102

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somali

94

0123456789abcdefghijklmnoqrstuwxyABCDEFGHIJKLMNOQRSTUWXY!”#$%&’()*+,-./:;<=>?@[]^_`{|}~°£€¥¢฿

spanish

116

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swahili

96

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swedish

106

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tagalog

103

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turkish

113

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uzbek_latin

102

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vietnamese

235

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welsh

102

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yoruba

121

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zulu

100

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russian

114

абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿₽

belarusian

116

абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ўіЎІ₽

ukrainian

114

абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ґіїєҐІЇЄ₴

tatar

126

абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿₽ӘәҖҗҢңӨөҮүҺһ

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

абвгдежзиклмнопрстуфхцчшАБВГДЕЖЗИКЛМНОПРСТУФХЦЧШјЈЂђЉљЊњЋћЏџ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿

uzbek_cyrillic

121

абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿ЎўҚқҒғҲҳ

greek

117

0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~αβγδεζηθικλμνξοπρστςυφχψωΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ£€¥¢฿άέήίϊΐόύϋΰώΆΈΉΊΪΌΎΫΏ·

greek_extended

312

0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~αβγδεζηθικλμνξοπρστςυφχψωΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ£€¥¢฿άέήίϊΐόύϋΰώΆΈΉΊΪΌΎΫΏ·ͶͷϜϝἀἁἂἃἄἅἆἇἈἉἊἋἌἍἎἏἐἑἒἓἔἕἘἙἚἛἜἝἠἡἢἣἤἥἦἧἨἩἪἫἬἭἮἯἰἱἲἳἴἵἶἷἸἹἺἻἼἽἾἿὀὁὂὃὄὅὈὉὊὋὌὍὐὑὒὓὔὕὖὗὙὛὝὟὠὡὢὣὤὥὦὧὨὩὪὫὬὭὮὯὰὲὴὶὸὺὼᾀᾁᾂᾃᾄᾅᾆᾇᾈᾉᾊᾋᾌᾍᾎᾏᾐᾑᾒᾓᾔᾕᾖᾗᾘᾙᾚᾛᾜᾝᾞᾟᾠᾡᾢᾣᾤᾥᾦᾧᾨᾩᾪᾫᾬᾭᾮᾯᾲᾳᾴᾶᾷᾺᾼῂῃῄῆῇῈῊῌῒΐῖῗῚῢΰῤῥῦῧῪῬῲῳῴῶῷῸῺῼ

hebrew

176

0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~אבגדהוזחטיךכלםמןנסעףפץצקרשתְֱֲֳִֵֶַָׇֹֺֻֽ־ֿ׀ׁׂ׃ׅׄ׆׳״֑֖֛֢֣֤֥֦֧֪֚֭֮֒֓֔֕֗֘֙֜֝֞֟֠֡֨֩֫֬֯ׯװױײיִﬞײַﬠﬡﬢﬣﬤﬥﬦﬧﬨ﬩שׁשׂשּׁשּׂאַאָאּבּגּדּהּוּזּטּיּךּכּלּמּנּסּףּפּצּקּרּשּתּוֹבֿכֿפֿﭏ₪

arabic

116

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persian

126

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urdu

134

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pashto

137

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kurdish

131

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uyghur

134

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sindhi

147

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devanagari

161

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hindi

161

0123456789कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~₹

sanskrit

161

0123456789कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~₹

marathi

161

0123456789कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~₹

nepali

161

0123456789कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~₹

gujarati

132

0123456789કખગઘઙચછજઝઞટઠડઢણતથદધનપફબભમયરલળવશષસહઅઆઇઈઉઊઋઌઍએઐઑઓઔ૦૧૨૩૪૫૬૭૮૯ઁંઃ઼ાિીુૂૃૄૅેૈૉોૌૢૣૺૻૼ૽૾૿્ઽ॥!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ૐ૰૱₹

bengali

127

0123456789কখগঘঙচছজঝঞটঠডঢণতথদধনপফবভমযরলশষসহড়ঢ়য়ৰৱৼঅআইঈউঊঋঌএঐওঔৠৡ০১২৩৪৫৬৭৮৯ািীুূৃেৈোৌৗ্ঽৎ৽৺৻!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ঁংঃ়৳₹

tamil

113

0123456789ஜஶஷஸஹகஙசஞடணதநபமயரலவழளறனஅஆஇஈஉஊஎஏஐஒஓஔ௦௧௨௩௪௫௬௭௮௯ாிீுூெேைொோௌ்௰௱௲!”#$%&’()*+,-./:;<=>?@[]^_`{|}~௳௴௵௶௷௸௹௺ஃௐ₹

telugu

129

0123456789కఖగఘఙచఛజఝఞటఠడఢణతథదధనపఫబభమయరఱలళవశషసహఴఅఆఇఈఉఊఋఌఎఏఐఒఓఔౠౡ౦౧౨౩౪౫౬౭౮౯౸౹౺౻ాిీుూృౄెేైొోౌౢౣ్ఽ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ఁంః₹

kannada

124

0123456789ಕಖಗಘಙಚಛಜಝಞಟಠಡಢಣತಥದಧನಪಫಬಭಮಯರಲವಶಷಸಹಳಅಆಇಈಉಊಋೠಌೡಎಏಐಒಓಔ೦೧೨೩೪೫೬೭೮೯ಾಿೀುೂೃೄೆೇೈೊೋೌ್।॥ೱೲ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ಂಃಁ₹

sinhala

129

0123456789කඛගඝඞඟචඡජඣඤඥඦටඨඩඪණඬතථදධනඳපඵබභමඹයරලවශෂසහළෆඅආඇඈඉඊඋඌඍඎඏඐඑඒඓඔඕඖ෦෧෨෩෪෫෬෭෮෯ාැෑිීුූෙේෛොෝෞ්෴!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ංඃ₹

malayalam

132

0123456789ൺൻർൽൾൿകഖഗഘങചഛജഝഞടഠഡഢണതഥദധനപഫബഭമയരറലളഴവശഷസഹഅആഇഈഉഊഋൠഌൡഎഏഐഒഓഔ൦൧൨൩൪൫൬൭൮൯ാിീുൂൃൄൢൣെേൈൊോൌ്!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ഃ൹ഽ൏ം₹

punjabi

122

0123456789ਕਖਗਘਙਚਛਜਝਞਟਠਡਢਣਤਥਦਧਨਪਫਬਭਮਯਰਲਵਸ਼ਸਹਖ਼ਗ਼ਜ਼ਫ਼ੜਲ਼ਅਆਇਈਉਊਏਐਓਔੲੳ੦੧੨੩੪੫੬੭੮੯ਂ਼ਾਿੀੁੂੇੈੋੌੑੰੱੵ੍।॥!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ੴ₹

odia

131

0123456789କଖଗଘଙଚଛଜଝଞଟଠଡଢଣତଥଦଧନପଫବଭମଯରଲଳଵଶଷସହୟୱଡ଼ଢ଼ଅଆଇଈଉଊଋଌଏଐଓଔୡୠ୦୧୨୩୪୫୬୭୮୯୲୳୴୵୶୷ାିୀୁୂୃୄେୈୋୌୢୣ୍ଽ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ଂଃଁ଼୰₹

khmer

144

0123456789កខគឃងចឆជឈញដឋឌឍណតថទធនបផពភមយរលវឝឞសហឡអឣឤឥឦឧឨឩឪឫឬឭឮឯឰឱឲឳ០១២៣៤៥៦៧៨៩ាិីឹឺុូួើឿៀេែៃោៅ្ំះៈ៉៊់៌៍៎៏័៑៓៝។៕៖៘៙៚ៗៜ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~៛

armenian

131

ԱԲԳԴԵԶԷԸԹԺԻԼԽԾԿՀՁՂՃՄՅՆՇՈՉՊՋՌՍՎՏՐՑՒՓՔՕՖՙՠաբգդեզէըթժիլխծկհձղճմյնշոչպջռսվտրցւփքօֆևֈ0123456789!”#$%&’()*+,-./:;<=>?@[]^_`{|}~՚՛՜՝՞՟։֊֏

sudanese

106

0123456789᮰᮱᮲᮳᮴᮵᮶᮷᮸᮹ᮊᮋᮌᮍᮎᮏᮐᮑᮒᮓᮔᮕᮖᮗᮘᮙᮚᮛᮜᮝᮞᮟᮠᮮᮯᮺᮻᮼᮽᮾᮿᮃᮄᮅᮆᮇᮈᮉᮀᮁᮂᮡᮢᮣᮤᮥᮦᮧᮨᮩ᮪᮫ᮬᮭ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~

thai

129

0123456789๐๑๒๓๔๕๖๗๘๙!”#$%&’()*+,-./:;<=>?@[]^_`{|}~๏๚๛ๆฯกขฃคฅฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลฦวศษสหฬอฮะาำเแโใไๅัิีึืฺุู็่้๊๋์ํ๎฿

lao

124

0123456789໐໑໒໓໔໕໖໗໘໙!”#$%&’()*+,-./:;<=>?@[]^_`{|}~ໆໞໟຯກຂຄຆງຈຉຊຌຍຎຏຐຑຒຓດຕຖທຘນບປຜຝພຟຠມຢຣລວຨຩສຫຬອຮະາຳຽເແໂໃໄໜໝັິີຶື຺ຸູົຼ່້໊໋໌ໍ

burmese

162

0123456789၀၁၂၃၄၅၆၇၈၉႐႑႒႓႔႕႖႗႘႙ကခဂဃငစဆဇဈဉညဋဌဍဎဏတထဒဓနပဖဗဘမယရလဝသဟဠအၐၑၒၓၔၕၚၛၜၝၡၥၦၮၯၰၵၶၷၸၹၺၻၼၽၾၿႀႁႎဣဤဥဦဧဩဪဿ့းံါာိီုူေဲဳဴဵျြွှ္်၊။၌၍၎၏ၤၗ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~

javanese

124

0123456789꧐꧑꧒꧓꧔꧕꧖꧗꧘꧙ꦏꦐꦑꦒꦓꦔꦕꦖꦗꦘꦙꦚꦛꦜꦝꦞꦟꦠꦡꦢꦣꦤꦥꦦꦧꦨꦩꦪꦫꦬꦭꦮꦯꦰꦱꦲꦄꦅꦆꦇꦈꦉꦊꦋꦌꦍꦎꦴꦵꦶꦷꦸꦹꦺꦻꦼꦀꦁꦂꦃ꦳ꦽꦾꦿ꧀꧈꧉꧊꧋꧌꧍ꧏ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~

georgian

177

0123456789ႠႡႢႣႤႥႦႧႨႩႪႫႬႭႮႯႰႱႲႳႴႵႶႷႸႹႺႻႼႽႾႿჀჁჂჃჄჅჇჍაბგდევზთიკლმნოპჟრსტუფქღყშჩცძწჭხჯჰჱჲჳჴჵჶჷჸჹჺჼჽჾჿᲐᲑᲒᲓᲔᲕᲖᲗᲘᲙᲚᲛᲜᲝᲞᲟᲠᲡᲢᲣᲤᲥᲦᲧᲨᲩᲪᲫᲬᲭᲮᲯᲰᲱᲲᲳᲴᲵᲶᲷᲸᲹᲺᲽᲾᲿ!”#$%&’()*+,-./:;<=>?@[]^_`{|}~჻₾

ethiopic

418

ሀሁሂሃሄህሆሇለሉሊላሌልሎሏሐሑሒሓሔሕሖሗመሙሚማሜምሞሟሠሡሢሣሤሥሦሧረሩሪራሬርሮሯሰሱሲሳሴስሶሷሸሹሺሻሼሽሾሿቀቁቂቃቄቅቆቇቈቊቋቌቍቐቑቒቓቔቕቖቘቚቛቜቝበቡቢባቤብቦቧቨቩቪቫቬቭቮቯተቱቲታቴትቶቷቸቹቺቻቼችቾቿኀኁኂኃኄኅኆኇኈኊኋኌኍነኑኒናኔንኖኗኘኙኚኛኜኝኞኟአኡኢኣኤእኦኧከኩኪካኬክኮኯኰኲኳኴኵኸኹኺኻኼኽኾዀዂዃዄዅወዉዊዋዌውዎዏዐዑዒዓዔዕዖዘዙዚዛዜዝዞዟዠዡዢዣዤዥዦዧየዩዪያዬይዮዯደዱዲዳዴድዶዷዸዹዺዻዼዽዾዿጀጁጂጃጄጅጆጇገጉጊጋጌግጎጏጐጒጓጔጕጘጙጚጛጜጝጞጟጠጡጢጣጤጥጦጧጨጩጪጫጬጭጮጯጰጱጲጳጴጵጶጷጸጹጺጻጼጽጾጿፀፁፂፃፄፅፆፇፈፉፊፋፌፍፎፏፐፑፒፓፔፕፖፗፘፙፚᎀᎁᎂᎃᎄᎅᎆᎇᎈᎉᎊᎋᎌᎍᎎᎏ፩፪፫፬፭፮፯፰፱፲፳፴፵፶፷፸፹፺፻፼0123456789፠፡።፣፤፥፦፧፨!”#$%&’()*+,-./:;<=>?@[]^_`{|}~£€¥¢฿

japanese

2383

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korean

11237

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simplified_chinese

6839

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