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Handwriting Recognition

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데이터셋

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IAM

The IAM database contains 13,353 images of handwritten lines of text created by 657 writers. The texts those writers transcribed are from the Lancaster-Oslo/Bergen Corpus of British English. It includes contributions from 657 writers making a total of 1,539 handwritten pages comprising of 115,320 words and is categorized as part of modern collection. The database is labeled at the sentence, line, and word levels. Source: Measuring Human Perception to Improve Handwritten Document Transcription Image Source: https://fki.tic.heia-fr.ch/databases/iam-handwriting-database

DeepWriting

A new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation. Source: DeepWriting: Making Digital Ink Editable via Deep Generative Modeling

RIMES

The RIMES database (Reconnaissance et Indexation de données Manuscrites et de fac similÉS / Recognition and Indexing of handwritten documents and faxes) was created to evaluate automatic systems of recognition and indexing of handwritten letters. Of particular interest are cases such as those sent by postal mail or fax by individuals to companies or administrations. The database was collected by asking volunteers to write handwritten letters in exchange of gift vouchers. Volunteer were given a fictional identity (same sex as the real one) and up to 5 scenarios. Each scenario has been chosen among 9 realistic following themes : change of personal information (address, bank account), information request, opening and closing (customer account), modification of contract or order, complaint (bad service quality…), payment difficulties (asking for a delay, tax exemption…), reminder letter, damage declaration with further circumstances and a destination (administrations or service providers (telephone, power, bank, insurances). The volunteers composed a letter with those pieces of information using their own words. The layout was free and it was only asked to use white paper and to write in a readable way with black ink. The collect was a success with more than 1,300 people who have participated to the RIMES database creation by writing up to 5 mails. The RIMES database thus obtained contains 12,723 pages corresponding to 5605 mails of two to three pages.

BanglaWriting

The BanglaWriting dataset contains single-page handwritings of 260 individuals of different personalities and ages. Each page includes bounding-boxes that bounds each word, along with the unicode representation of the writing. This dataset contains 21,234 words and 32,787 characters in total. Moreover, this dataset includes 5,470 unique words of Bangla vocabulary. Apart from the usual words, the dataset comprises 261 comprehensible overwriting and 450 incomprehensible overwriting. All of the bounding boxes and word labels are manually-generated. The dataset can be used for complex optical character/word recognition, writer identification, and handwritten word segmentation. Furthermore, this dataset is suitable for extracting age-based and gender-based variation of handwriting. Source: https://github.com/QuwsarOhi/BanglaWriting Image Source: https://github.com/QuwsarOhi/BanglaWriting

READ 2016

This dataset arises from the READ project (Horizon 2020). The dataset consists of a subset of documents from the Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805 (about 30.000 pages), which will be used in the READ project. This dataset is written in Early Modern German. The number of writers is unknown. Handwriting in this collection is complex enough to challenge the HTR software. The training dataset is composed of 400 pages; most of the pages consist of a single block with many difficulties for line detection and extraction. The ground-truth in this set is in PAGE format and it is provided annotated at line level in the PAGE files. The previous dataset is the same that is located at https://zenodo.org/record/218236#.WnLhaCHhBGF The new file includes the test set corresponding to the HTR competition held at ICFHR 2016 Toselli, A.H., Romero, V., Villegas, M., Vidal, E., & Sánchez, J.A. (2018). HTR Dataset ICFHR 2016 (Version 1.2.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1297399

Bentham

Bentham manuscripts refers to a large set of documents that were written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). Volunteers of the Transcribe Bentham initiative transcribed this collection. Currently, >6 000 documents or > 25 000 pages have been transcribed using this public web platform. For our experiments, we used the BenthamR0 dataset a part of the Bentham manuscripts.

HKR

The database is written in Cyrillic and shares the same 33 characters. Besides these characters, the Kazakh alphabet also contains 9 additional specific characters. This dataset is a collection of forms. The sources of all the forms in the datasets were generated by LATEX which subsequently was filled out by persons with their handwriting. The database consists of more than 1400 filled forms. There are approximately 63000 sentences, more than 715699 symbols produced by approximately 200 diferent writers. We utilized three different datasets described as following: Handwritten samples (Forms) of keywords in Kazakh and Russian (Areas, Cities , Village , etc.) Handwritten Kazakh and Russian alphabet in cyrillic Handwritten samples (Forms) of poems in Russian Image source: https://github.com/abdoelsayed2016/HKR_Dataset

BanglaLekha-Isolated

This dataset contains Bangla handwritten numerals, basic characters and compound characters. This dataset was collected from multiple geographical location within Bangladesh and includes sample collected from a variety of aged groups. This dataset can also be used for other classification problems i.e: gender, age, district. Source: BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset

BRUSH

The BRUSH dataset (BRown University Stylus Handwriting) contains 27,649 online handwriting samples from a total of 170 writers. Every sequence is labeled with intended characters such that dataset users can identify to which character a point in a sequence corresponds. The dataset was introduced in the paper "Generating Handwriting via Decoupled Style Descriptors" by Atsunobu Kotani, Stefanie Tellex, James Tompkin from Brown University, presented at European Conference on Computer Vision (ECCV) 2020. ##### Terms of Use The BRUSH dataset may only be used for non-commercial research purposes. Anyone wanting to use it for other purposes should contact Prof. James Tompkin. If you publish materials based on this database, we request that you please include a reference to our paper. @inproceedings{kotani2020generating, title={Generating Handwriting via Decoupled Style Descriptors}, author={Kotani, Atsunobu and Tellex, Stefanie and Tompkin, James}, booktitle={European Conference on Computer Vision}, pages={764--780}, year={2020}, organization={Springer} }

KOHTD

Kazakh offline Handwritten Text dataset (KOHTD) has 3000 handwritten exam papers and more than 140335 segmented images and there are approximately 922010 symbols. It can serve researchers in the field of handwriting recognition tasks by using deep and machine learning. Image source: https://github.com/abdoelsayed2016/KOHTD

Konzil

Konzil dataset was created by specialists of the University of Greifswald. It contains manuscripts written in modern German. Train sample consists of 353 lines, validation - 29 lines and test - 87 lines.

Patzig

Patzig contains handwritten texts written in modern German. Train sample consists of 485 lines, validation - 38 lines and test -118 lines.

Ricordi

Ricordi contains handwritten texts written in Italian. Train sample consists of 295 lines, validation - 19 lines and test - 69 lines.

Schiller

Schiller contains handwritten texts written in modern German. Train sample consists of 244 lines, validation - 21 lines and test - 63 lines.

Schwerin

Schwerin contains handwritten texts written in medieval German. Train sample consists of 793 lines, validation - 68 lines and test - 196 lines.

BN-HTRd

We introduce a new Dataset (BN-HTRd) for offline Handwritten Text Recognition (HTR) from images of Bangla scripts comprising words, lines, and document-level annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus - which acted as ground truth texts for the handwritings. Our dataset contains a total of 786 full-page images collected from 150 different writers. With a staggering 1,08,181 instances of handwritten words, distributed over 14,383 lines and 23,115 unique words, this is currently the 'largest and most comprehensive dataset' in this field. We also provided the bounding box annotations (YOLO format) for the segmentation of words/lines and the ground truth annotations for full-text, along with the segmented images and their positions. The contents of our dataset came from a diverse news category, and annotators of different ages, genders, and backgrounds, having variability in writing styles. The BN-HTRd dataset can be adopted as a basis for various handwriting classification tasks such as end-to-end document recognition, word-spotting, word/line segmentation, and so on. The statistics of the original dataset are given below: - Number of writers = 150 - Total number of images = 786 - Total number of lines = 14,383 - Total number of words = 1,08,181 - Total number of unique words = 23,115 - Total number of punctuation = 7,446 - Total number of characters = 5,74,203 From v3.0 onwards, we are also providing automatic bounding box annotations (YOLO format) of 805 document images containing words/lines. The statistics of the automatic annotations are given below: - Number of writers = 87 - Total number of images = 805 - Total number of lines = 14,836 - Total number of words = 1,06,135

Saint Gall

Saint Gall dataset contains handwritten historical manuscripts written in Latin that date back to the 9th century. It consists of 60 pages, 1 410 text lines and 11 597 words.

An extensive dataset of handwritten central Kurdish isolated characters

Data collection: Finding a suitable source of data is considered a first step toward building a database. The first step in building a database is finding a suitable source. Here, the main goal is to collect images of Kurdish handwritten characters written by many writers. So, a form is designed to do so. The form is shown in Figure 1. It consists of 1 alphabet at a time letter that has been printed on the top right corner, and it has 125 empty blocks. The writers have been asked to write each letter three times in the three empty blocks. The total number of writers is 390. The forms have been distributed among two main categories: The academic staff of the Information Technology department at Tishk International University, the university students of the University of Kurdistan-Hawler, Salahaddin University, and Tishk International University As shown in Table 2. In total there were ten sets of forms, each set with 35 forms for 35 different letters, at first, we decided that nine sets, which will give us at least 1100 images for each letter were the best option for the time that we had. Then there were some problems with the collection process, in first prints of the forms there was confusion for instance in Set 2, there were 2 forms for the letter (چ) and none for (ج), and since we printed and distributed the form at the same time, we were not aware of this problem until the stage of pre-processing, This was creating an inconsistency in the number of samples that we had, for example by the 9th set we had 504 images of the letter (ڤ), which was much less than other letters that they had at least 1000 images. So we decided to add the 10th set as a complementary to other sets, it only contained those letter, which was missing in the first 9 forms, which was (ز،ژ،ش،غ،ڤ،ق،ک،ل،ن،ی), as explained in Table 3, the First column is the letter and columns 2-11 represent several images gathered in each set accordingly, while the first row the header row 2-36 are letters in each set, last row, and last columns are for the total of each letter and each set. Labeling and Organizing : Each image is labeled with three numbers and separated by an underscore, the first number is the id of the letter according to its positing in the alphabetical order which is shown in Table 4, the second number being the number of the set of form which there was 10 sets each giving to a specific group of writers, the third number is the order of that character in the form which was between 1 to 126, so each image had a label like following 02_01_94.jpg, 02 is the order of the letter which in this case is Alef (١), then 01 being in the set number 1 which was given to 4th-grade students of Information Technology department in Tishk International University, and 94 is the order of that image in the form. Each letter was stored in a folder with its ID as the name of that folder, with each folder containing approximately 1134 images of that letter.

Calliar

Calliar is a dataset for Arabic calligraphy. The dataset consists of 2500 json files that contain strokes manually annotated for Arabic calligraphy.

KHATT

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

MatriVasha:

MatriVasha the largest dataset of handwritten Bangla compound characters for research on handwritten Bangla compound character recognition. The proposed dataset contains 120 different types of compound characters that consist of 306,464‬ images written where 152,950 male and 153,514 female handwritten Bangla compound characters. This dataset can be used for other issues such as gender, age, district base handwriting research because the sample was collected that included district authenticity, age group, and an equal number of men and women.

Burmese Handwritten Digit Dataset (BHDD)

The Burmese Handwritten Digit Dataset (BHDD) is a dataset project specifically created for recognizing handwritten Burmese digits. It is a Burmese version of MNIST dataset with a training set of 60,000 examples, and a test set of 27,561 examples. ## Overview Dataset Statistics: - Training Set: 60,000 samples - Testing Set: 27,561 samples - Number of Classes: 10 (Burmese digits 0–9) Data Format: - Train Image Shape: (60000, 784) - Train Label Shape: (60000, 10) - Test Image Shape: (27561, 784) - Test Label Shape: (27561, 10) The dataset was collected from over 150 individuals of different ages (ranging from high school students to professionals in their 50s) and diverse occupations (including clerks, programmers, and others) to achieve a wide variety of handwriting styles. We then preprocessed to mirror the structure and functionality of MNIST. ## Dataset Content The dataset consists of: 1. Train Images: 60,000 grayscale images of handwritten Burmese digits, flattened into a 1D array of size 784 (28x28 pixels). 2. Train Labels: One-hot encoded labels corresponding to the digit class. 3. Test Images: 27,561 grayscale images for testing purposes. 4. Test Labels: One-hot encoded labels for testing data. ## Contribution We encourage the ML/DL community to contribute by: - Creating digit recognizers. - Benchmarking with different models and algorithms. - Writing tutorials and sharing findings. ## Citation If you use the BHDD dataset in your work, please cite this repository: @dataset{bhdd, author = {Expa.AI Research Team}, title = {Burmese Handwritten Digit Dataset (BHDD)}, year = {2019}, url = {https://github.com/baseresearch/BHDD} } ## Acknowledgments This dataset would not have been possible without: - The efforts of the Expa.AI Research Team. - Volunteers and interns from Taungoo Computer University who contributed handwriting samples. - Highschool students from St.Augustine / B.E.H.S (2) Kamayut - Friends and family members of Expa.AI Research Team. - The community’s ongoing support and interest in ML/DL for the Burmese language. ## License This dataset is released under the LGPL-3.0 license. Please see the LICENSE file for more details.

DigiLeTs

A dataset with $23\,870$ digital trajectories (i.e. time series) of handwritten lower- and uppercase Latin letters and Arabic numbers ($a$-$z$, $A$-$Z$, $0$-$9$), generated by $77$ experts using a Wacom Pen Tablet. An expert is considered a proficient user of the recorded symbols, in this case adult native German speakers. DigiLetTs was created to extend the Omniglot dataset and contains five variants per character per subject to allow the quantification of intra-subject variability and to assess and account for individual writing styles. The determination and imitation of subject-dependent writing styles is introduced as a new task in this paper. For more information about the dataset, please refer to the repository (Homepage button below).

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