Dataset

This dataset was created by dillsunnyb11

Released under Database: Open Database, Contents: Database Contents

Contents

Digit Recognizer

## Overview

Digit Recognizer is a dataset for object detection tasks - it contains Digits annotations for 221 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).

jasminem09/mnist-digit-recognizer dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Yuvam21

Contents

Seven Segment Digit Recognition

## Overview

Seven Segment Digit Recognition is a dataset for object detection tasks - it contains Digits annotations for 213 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).

CyberCraze/digit-recognizer dataset hosted on Hugging Face and contributed by the HF Datasets community

Handwritten Digit Recognition

## Overview

Handwritten Digit Recognition is a dataset for object detection tasks - it contains Test annotations for 3,966 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 [MIT license](https://creativecommons.org/licenses/MIT).

Dataset

This dataset was created by Angga Yulian Adi Pradana

Released under Apache 2.0

Contents

sunildkumar/digit-recognition-tool-use-r1 dataset hosted on Hugging Face and contributed by the HF Datasets community

250

The CVL Single Digit dataset consists of 7000 single digits (700 digits per class) written by approximately 60 different writers. The validation set has the same size but different writers. The validation set may be used for parameter estimation and validation but not for supervised training. The CVL Digit Strings dataset uses 10 different digit strings from a total of about 120 writers resulting in 1262 training images. The digits from the CVL Single Digit dataset were extracted from these strings.

This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to:

Markus Diem, Stefan Fiel, Angelika Garz, Manuel Keglevic, Florian Kleber and Robert Sablatnig, ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013), In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 1454-1459, 2013.

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.

Dataset

This dataset was created by Aaron

Contents

Digit Recognition

## Overview

Digit Recognition is a dataset for object detection tasks - it contains 0 1 2 3 4 5 6 7 8 9 Null annotations for 908 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 [MIT license](https://creativecommons.org/licenses/MIT).

sunildkumar/digit-recognition dataset hosted on Hugging Face and contributed by the HF Datasets community

Digit Recognition Maf

## Overview

Digit Recognition Maf is a dataset for instance segmentation tasks - it contains Digits annotations for 1,655 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).

A benchmark dataset is required for the development of an efficient and a reliable recognitionsystem. Unfortunately, no comprehensive benchmark dataset exists for handwritten Devnagari opticaldocument recognition research, at least in the public domain. This paper is an effort in this direction. In here,we introduce a comprehensive dataset that we referred to as CPAR-2012 dataset, for such benchmark studies,also present some preliminary recognition results. The dataset includes 35,000 isolated handwritten numerals,83,300 characters, 2,000 constrained and 2,000 unconstrained handwritten pangrams. It is organized in arelational data model that contains text images along with their writer's information and related handwritingattributes. We collected the handwriting samples from 2,000 subjects who were chosen from different age,ethnicity, and educational background, regional and linguistic groups. The samples reflect expected variationsin Devnagari handwriting. The digit recognition results using recognition schemes that uses simple mostfeatures & four neural network classifiers & KNN, and classifier ensemble have also been reported forbenchmarking.

Dzongkha, the national language of Bhutan, has limited resources available for Natural Language Processing (NLP) tasks because the language is relatively understudied. However, there is no publicly available benchmark dataset for handwritten character identification in the Dzongkha digit script. The dataset contains 1000 images of handwritten Dzongkha digits that are captured using Google Jamboard in JPG format. The image data is assembled from a total of 100 indigenous and non-indigenous people of Bhutan irrespective of age, gender, educational background, etc. In the designed dataset, there are 10 different classes of Dzongkha digits which range from 0 to 9. The labels of these classes are: 0 (༠), 1 (༡), 2 (༢), 3 (༣), 4 (༤), 5 (༥), 6 (༦), 7 (༧), 8 (༨), 9 (༩).

Dataset

This dataset was created by Faycal Zouine

Contents

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">