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README.md

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 # Dataset Card for "notMNIST"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## Overview
+
+The notMNIST dataset is a collection of images of letters from A to J in various fonts. It is designed as a more challenging alternative to the traditional MNIST dataset, which consists of handwritten digits. The notMNIST dataset is commonly used in machine learning and computer vision tasks for character recognition.
+
+## Dataset Information
+
+Number of Classes: 10 (A to J)
+Number of Samples: 187,24
+Image Size: 28 x 28 pixels
+Color Channels: Grayscale
+
+## Dataset Structure
+
+The dataset is split into a training set and a test set. Each class has its own subdirectory containing images of that class. The directory structure is as follows:
+
+notMNIST/
+|-- train/
+|   |-- A/
+|   |-- B/
+|   |-- ...
+|   |-- J/
+|
+|-- test/
+|   |-- A/
+|   |-- B/
+|   |-- ...
+|   |-- J/
+
+
+## Acknowledgements
+
+http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html
+https://www.kaggle.com/datasets/lubaroli/notmnist
+
+
+## Inspiration
+
+This is a pretty good dataset to train classifiers! According to Yaroslav:
+
+  Judging by the examples, one would expect this to be a harder task
+  than MNIST. This seems to be the case -- logistic regression on top of
+  stacked auto-encoder with fine-tuning gets about 89% accuracy whereas
+  same approach gives got 98% on MNIST. Dataset consists of small
+  hand-cleaned part, about 19k instances, and large uncleaned dataset,
+  500k instances. Two parts have approximately 0.5% and 6.5% label error
+  rate. I got this by looking through glyphs and counting how often my
+  guess of the letter didn't match it's unicode value in the font file.
+
+