Handwriting generation

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

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

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} }

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

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

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

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

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.

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).