Ishihara-Like MNIST Dataset

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Ishihara-Like MNIST Dataset

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Ishihara-Like MNIST Dataset

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Ishihara-Like MNIST

Description

Explore the Ishihara-Like MNIST Dataset—digitally generated color-obfuscated images based on the MNIST dataset. Ideal for machine learning.

Description:

Color blindness, a condition that affects approximately 8% of men and 0.5% of women worldwide, hinders individuals from perceiving certain colors accurately. One of the most common tests for diagnosing color blindness is the Ishihara color blindness test, which uses a series of plates containing numerals obscured by dots of various colors. The test relies on the color contrast between the numeral and the background, designed to exploit the vision deficiency and reveal a person’s inability to distinguish the hidden numbers.

Inspired by this concept, we developed the Ishihara-Like MNIST dataset. This dataset is generated by applying the principles of the Ishihara test to the widely known MNIST dataset of handwritten digits. Each image in the dataset contains a numeral from MNIST that has been creatively obscured with colored dots, closely mimicking the structure of an Ishihara test plate. The result is a set of images where the digits are embedded in a visually complex background, challenging a machine learning model’s ability to recognize and classify them.

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Dataset Details:

Structure: The dataset consists of both training and testing sets, each containing thousands of Ishihara-like images. These images are generated from the original MNIST images by overlaying colored dots to obscure the boundaries of the digits.
Image Size: Each image retains the original dimensions of 28×28 pixels, maintaining compatibility with MNIST-related tasks.
Use Cases:
- Visual Perception Studies: Analyzing how models perform when color obfuscation is present.
- Accessibility Research: Studying AI’s capability to enhance the experience of color-blind individuals.
- Advanced Machine Learning Tasks: Training models that must overcome visual distortions and complexities.

Potential Applications:

- - Development of robust character recognition algorithms in visually complex environments.
  - Enhancing accessibility features for color-blind individuals in AI-based systems.
  - Use in CAPTCHA systems to increase complexity for automated bots while maintaining human readability.