The MNIST database of handwritten digits.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/mnist-3.0.1.png" alt="Visualization" width="500px">

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.

The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning. It was created by "re-mixing" the samples from NIST's original datasets. The creators felt that since NIST's training dataset was taken from American Census Bureau employees, while the testing dataset was taken from American high school students, it was not well-suited for machine learning experiments. Furthermore, the black and white images from NIST were normalized to fit into a 28x28 pixel bounding box and anti-aliased, which introduced grayscale levels.

Yann LeCun, Courant Institute, NYU Corinna Cortes, Google Labs, New York Christopher J.C. Burges, Microsoft Research, Redmond

Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('fashion_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/fashion_mnist-3.0.1.png" alt="Visualization" width="500px">

Context

Handwritten image data is easy to find in languages such as English and Japanese, but not for many Indian languages including Hindi. While trying to create an MNIST like personal project, I stumbled upon a Hindi Handwritten characters dataset by Shailesh Acharya and Prashnna Kumar Gyawali, which is uploaded to the UCI Machine Learning Repository.

This dataset however, only has the digits from 0 to 9, and all other characters have been removed.

Content

Data Type: GrayScale Image Image Format: PNG Resolution: 32 by 32 pixels Actual character is centered within 28 by 28 pixel, padding of 2 pixel is added on all four sides of actual character.

There are ~1700 images per class in the Train set, and around ~300 images per class in the Test set.

Acknowledgements

The Dataset is ©️ Original Authors.

Original Authors: - Shailesh Acharya - Prashnna Kumar Gyawali

Citation: S. Acharya, A.K. Pant and P.K. Gyawali “**Deep Learning Based Large Scale Handwritten Devanagari Character Recognition**”, In Proceedings of the 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 121-126, 2015.

The full Dataset is available here: https://archive.ics.uci.edu/ml/datasets/Devanagari+Handwritten+Character+Dataset

Moving variant of MNIST database of handwritten digits. This is the data used by the authors for reporting model performance. See tfds.video.moving_mnist.image_as_moving_sequence for generating training/validation data from the MNIST dataset.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('moving_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

The not-MNIST dataset is a dataset of handwritten digits. It is a challenging dataset that can be used for machine learning and artificial intelligence research. The dataset consists of 100,000 images of handwritten digits. The images are divided into a training set of 60,000 images and a test set of 40,000 images. The images are drawn from a variety of fonts and styles, making them more challenging than the MNIST dataset. The images are 28x28 pixels in size and are grayscale. The dataset is available under the Creative Commons Zero Public Domain Dedication license.

Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST shares the same image size, data format and the structure of training and testing splits with the original MNIST.

The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 (https://www.nist.gov/srd/nist-special-database-19) and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset (http://yann.lecun.com/exdb/mnist/). Further information on the dataset contents and conversion process can be found in the paper available at https://arxiv.org/abs/1702.05373v2

The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems. Contributing to its widespread adoption are the understandable and intuitive nature of the task, its relatively small size and storage requirements and the accessibility and ease-of-use of the database itself. The MNIST database was derived from a larger dataset known as the NIST Special Database 19 which contains digits, uppercase and lowercase handwritten letters. This paper introduces a variant of the full NIST dataset, which we have called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset. The result is a set of datasets that constitute a more challenging classification tasks involving letters and digits, and that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems. Benchmark results are presented along with a validation of the conversion process through the comparison of the classification results on converted NIST digits and the MNIST digits.

The database is made available in original MNIST format and Matlab format.

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. 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. With some classification methods (particuarly template-based methods, such as SVM and K-nearest neighbors),

MNIST-M is created by combining MNIST digits with the patches randomly extracted from color photos of BSDS500 as their background. It contains 59,001 training and 90,001 test images.

MNIST dataset originally hosted on https://deeplearning.net, re-hosted here because deeplearning.net is currently inaccessible.

Recording of the MNIST dataset displayed on a screen as viewed by a dynamic vision sensor moving through a fixed trajectory on a pan-tilt unit. Details are in the listed paper.

• This dataset contains training and testing data for digit recognition which includes hand written images of digits.
  • It contains four zip files which you can easily include in your neural network. So, download all four of them by clicking "Download all" button.
  • This is the MNIST dataset used world-wide to check the performance of neural networks based upon digit recognition.
  • It also contains training and testing labels.

CI-MNIST (Correlated and Imbalanced MNIST) is a variant of MNIST dataset with introduced different types of correlations between attributes, dataset features, and an artificial eligibility criterion. For an input image x, the label y∈{1,0} indicates eligibility or ineligibility, respectively, given that x is even or odd. The dataset defines the background colors as the protected or sensitive attribute s∈{0,1}, where blue denotes the unprivileged group and red denotes the privileged group. The dataset was designed in order to evaluate bias-mitigation approaches in challenging setups and be capable of controlling different dataset configurations.

The MNIST-100 dataset is a specialized subset of the renowned MNIST dataset, designed for focused research and applications in digit recognition.

A specific binarization of the MNIST images originally used in (Salakhutdinov & Murray, 2008). This dataset is frequently used to evaluate generative models of images, so labels are not provided.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('binarized_mnist', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/binarized_mnist-1.0.0.png" alt="Visualization" width="500px">

A dataset containing images of handwritten english numerals from 0-9 obtained from National Institute of Standards and Technology. It consists of greyscale images of handwritten digits and consists of 60000 images of size 28*28 for training and 10000 images as test examples.

The MultiMNIST dataset is generated from MNIST. The training and tests are generated by overlaying a digit on top of another digit from the same set (training or test) but different class. Each digit is shifted up to 4 pixels in each direction resulting in a 36×36 image. Considering a digit in a 28×28 image is bounded in a 20×20 box, two digits bounding boxes on average have 80% overlap. For each digit in the MNIST dataset 1,000 MultiMNIST examples are generated, so the training set size is 60M and the test set size is 10M.

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F769452%2Ff6e2d0f05093e42a67119bde723b24d5%2Fdata-original.png?generation=1600931282565624&alt=media" alt="">

The Chinese MNIST dataset uses data collected in the frame of a project at Newcastle University.

Project Description

One hundred Chinese nationals took part in data collection. Each participant wrote with a standard black ink pen all 15 numbers in a table with 15 designated regions drawn on a white A4 paper. This process was repeated 10 times with each participant. Each sheet was scanned with a resolution of 300x300 pixels. It resulted in a dataset of 15000 images, each representing one character from a set of 15 characters (grouped in samples, grouped in suites, with 10 samples/volunteer and 100 volunteers).

Further Data Processing

The project was originally downloaded from the original project page the raw images. This dataset is the CSV version of the original dataset uploaded to Kaggle by Gabriel Preda. The original Chinese MNIST dataset uploaded by him can be found at the following LINK. The only difference is that this dataset contains all the images and labels in the same unique file.

Content

The dataset contains the following:

• a unique CSV file: chineseMNIST.csv

This file contains the 15000 observations and 4098 columns. Columns 1 to 4096 represent each pixel of the image (64x64). The last two columns denote the value label and the original Chinese character. The following image shows the unique labels and characters https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F769452%2F61c54df3540346d4b56cd611ba41143d%2Fchanracter_mapping.png?generation=1596618751340901&alt=media" alt="">

Acknowledgements

The original dataset from Kaggle was uploaded by Gabriel Preda. See the original Chinese MNIST dataset. The following authors collected the data: Dr. K Nazarpour and Dr. M Chen from Newcastle University.

Nazarpour, K; Chen, M (2017): Handwritten Chinese Numbers. Newcastle University. Dataset. https://doi.org/10.17634/137930-3

Inspiration

You can use this data the same way you used MNIST, KMNIST of Fashion MNIST: refine your image classification skills, use GPU & TPU to implement CNN architectures for models to perform such multiclass classifications.