Handwritten Digit Recognition

The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. Source: http://yann.lecun.com/exdb/mnist/ Image Source: https://en.wikipedia.org/wiki/MNIST_database#/media/File:MnistExamples.png

The DIGITS dataset consists of 1797 8×8 grayscale images (1439 for training and 360 for testing) of handwritten digits. Source: Differentially Private Variational Dropout Image Source: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html

To benchmark Bengali digit recognition algorithms, a large publicly available dataset is required which is free from biases originating from geographical location, gender, and age. With this aim in mind, NumtaDB, a dataset consisting of more than 85,000 images of hand-written Bengali digits, has been assembled.

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.

MNIST-MIX is a multi-language handwritten digit recognition dataset. It contains digits from 10 different languages. Source: https://github.com/jwwthu/MNIST-MIX

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.

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).