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 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">

250

Description: 👉 Download the dataset here Discover the rich and intricate patterns of Persian handwritten digits with our extensive dataset, thoughtfully curated to provide an unparalleled resource for Al and machine learning applications. This comprehensive collection comprises 150,000 high-resolution images, each meticulously generated to represent the full spectrum of Persian digits from 0 to 9. Leveraging advanced Generative Adversarial Networks (GANs), these images capture the subtle… See the full description on the dataset page: https://huggingface.co/datasets/gtsaidata/Persian-Handwritten-Digits-Dataset.

Explore the Persian Handwritten Digits Dataset featuring 150,000 GAN-generated images of digits 0-9. Perfect for digit recognition, generative modeling, and OCR systems.

The MNIST dataset is a collection of images of handwritten digits, with size n = 70,000 and D = 784.

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

Credit: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998

This is a subset of MNIST handwritten digits dataset (http://yann.lecun.com/exdb/mnist/). Training data of composed of 12,000 images of digits 0 to 9. Test data is composed of 6,000 images of digits 0 to 9 (Original dataset has 60,000 training and 10,000 testing images. We are using a subset for a Galaxy tutorial, so the training is not too computationally intensive). Images are grayscale and 28 by 28 pixels. Each pixel has a value between 0 and 255 (0 for color black, 255 for color white, and all other values for different shades of gray).

Arabic Handwritten Digits DatasetAbstractIn recent years, handwritten digits recognition has been an important areadue to its applications in several fields. This work is focusing on the recognitionpart of handwritten Arabic digits recognition that face several challenges, includingthe unlimited variation in human handwriting and the large public databases. Thepaper provided a deep learning technique that can be effectively apply to recognizing Arabic handwritten digits. LeNet-5, a Convolutional Neural Network (CNN)trained and tested MADBase database (Arabic handwritten digits images) that contain 60000 training and 10000 testing images. A comparison is held amongst theresults, and it is shown by the end that the use of CNN was leaded to significantimprovements across different machine-learning classification algorithms.The Convolutional Neural Network was trained and tested MADBase database (Arabic handwritten digits images) that contain 60000 training and 10000 testing images. Moreover, the CNN is giving an average recognition accuracy of 99.15%.ContextThe motivation of this study is to use cross knowledge learned from multiple works to enhancement the performance of Arabic handwritten digits recognition. In recent years, Arabic handwritten digits recognition with different handwriting styles as well, making it important to find and work on a new and advanced solution for handwriting recognition. A deep learning systems needs a huge number of data (images) to be able to make a good decisions.ContentThe MADBase is modified Arabic handwritten digits database contains 60,000 training images, and 10,000 test images. MADBase were written by 700 writers. Each writer wrote each digit (from 0 -9) ten times. To ensure including different writing styles, the database was gathered from different institutions: Colleges of Engineering and Law, School of Medicine, the Open University (whose students span a wide range of ages), a high school, and a governmental institution.MADBase is available for free and can be downloaded from (http://datacenter.aucegypt.edu/shazeem/) .AcknowledgementsCNN for Handwritten Arabic Digits Recognition Based on LeNet-5http://link.springer.com/chapter/10.1007/978-3-319-48308-5_54Ahmed El-Sawy, Hazem El-Bakry, Mohamed LoeyProceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016Volume 533 of the series Advances in Intelligent Systems and Computing pp 566-575InspirationCreating the proposed database presents more challenges because it deals with many issues such as style of writing, thickness, dots number and position. Some characters have different shapes while written in the same position. For example the teh character has different shapes in isolated position.Arabic Handwritten Characters Datasethttps://www.kaggle.com/mloey1/ahcd1Benha Universityhttp://bu.edu.eg/staff/mloeyhttps://mloey.github.io/

Handwritten Digits

## Overview

Handwritten Digits is a dataset for object detection tasks - it contains Digits annotations for 1,560 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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 effort on preprocessing and formatting.

Four files are available on this site: train-images-idx3-ubyte.gz: training set images (9912422 bytes) train-labels-idx1-ubyte.gz: training set labels (28881 bytes) t10k-images-idx3-ubyte.gz: test set images (1648877 bytes) t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)

The MNIST-100 dataset is a variation of the original MNIST dataset, consisting of 100 handwritten numbers extracted from the MNIST dataset. Unlike the traditional MNIST dataset, which contains 60,000 training images of digits from 0 to 9, the Modified MNIST-10 dataset focuses on 100 numbers. Dataset Overview:

Dataset Name: MNIST-100 Total Number of Images: train: 60000 test: 1000 Classes: 100 (Numbers from 00 to 99) Image Size: 28x56 pixels (grayscale)

Data Collection: The MNIST-100 dataset… See the full description on the dataset page: https://huggingface.co/datasets/marcin119a/mnist100.

Dataset

This dataset was created by Bipul Nath

Contents

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.

Dataset

This dataset was created by Tabarka Rajab

Contents

The dataset used in the paper is MNIST Fashion and MNIST Handwritten digits.

MNIST is a picture data set of handwritten numbers, which was organized by the National Institute of Standards and Technology (NIST) of the United States. A total of 250 handwritten digital pictures were collected, 50% of which were high school students and 50% were from the staff of the Census Bureau. The collection purpose of this data set is to realize the recognition of handwritten digits through algorithms. The data set contains 60000 images and labels, while the test set contains 10000 images and labels. The first 5000 training sets from the initial NIST program, The last 5000 test sets from the original NIST program. The first 5000 are more regular than the last 5000, because the first 5000 data come from the employees of the US Census Bureau, and the last 5000 data come from college students.

How to load? train_data = np.loadtxt('/kaggle/input/mnistdata/mnist_train_images', dtype=np.uint16) train_labels = np.loadtxt('/kaggle/input/mnistdata/mnist_train_labels', dtype=np.uint8) test_data = np.loadtxt('/kaggle/input/mnistdata/mnist_test_images', dtype=np.uint16) test_labels = np.loadtxt('/kaggle/input/mnistdata/mnist_test_labels', dtype=np.uint8)

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.

Database description:

The written and spoken digits database is not a new database but a constructed database from existing ones, in order to provide a ready-to-use database for multimodal fusion [1].

The written digits database is the original MNIST handwritten digits database [2] with no additional processing. It consists of 70000 images (60000 for training and 10000 for test) of 28 x 28 = 784 dimensions.

The spoken digits database was extracted from Google Speech Commands [3], an audio dataset of spoken words that was proposed to train and evaluate keyword spotting systems. It consists of 105829 utterances of 35 words, amongst which 38908 utterances of the ten digits (34801 for training and 4107 for test). A pre-processing was done via the extraction of the Mel Frequency Cepstral Coefficients (MFCC) with a framing window size of 50 ms and frame shift size of 25 ms. Since the speech samples are approximately 1 s long, we end up with 39 time slots. For each one, we extract 12 MFCC coefficients with an additional energy coefficient. Thus, we have a final vector of 39 x 13 = 507 dimensions. Standardization and normalization were applied on the MFCC features.

To construct the multimodal digits dataset, we associated written and spoken digits of the same class respecting the initial partitioning in [2] and [3] for the training and test subsets. Since we have less samples for the spoken digits, we duplicated some random samples to match the number of written digits and have a multimodal digits database of 70000 samples (60000 for training and 10000 for test).

The dataset is provided in six files as described below. Therefore, if a shuffle is performed on the training or test subsets, it must be performed in unison with the same order for the written digits, spoken digits and labels.

Files:

data_wr_train.npy: 60000 samples of 784-dimentional written digits for training;

data_sp_train.npy: 60000 samples of 507-dimentional spoken digits for training;

labels_train.npy: 60000 labels for the training subset;

data_wr_test.npy: 10000 samples of 784-dimentional written digits for test;

data_sp_test.npy: 10000 samples of 507-dimentional spoken digits for test;

labels_test.npy: 10000 labels for the test subset.

References:

Khacef, L. et al. (2020), "Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning".

LeCun, Y. & Cortes, C. (1998), “MNIST handwritten digit database”.

Warden, P. (2018), “Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition”.