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README.md

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 # AlphaNum Dataset
 
 ## Abstract
-The AlphaNum dataset, curated by Louis Rädisch, is an extensive repository of grayscale, handwritten characters and numerals, each of 28x28 pixel dimensions. This dataset is designed to support Optical Character Recognition (OCR) tasks, offering labels that range from 33 to 126, and 999, aligning with ASCII characters from '!' to '~', and 'null', respectively. The 'null' category includes images generated through a noise injection process, resulting in normally distributed light pixels placed randomly.
+The AlphaNum dataset, curated by Louis Rädisch, is an extensive repository of grayscale, handwritten characters and numerals, each of 24x24 pixel dimensions. This dataset is designed to support Optical Character Recognition (OCR) tasks, offering labels that range from 33 to 126, and 999, aligning with ASCII characters from '!' to '~', and 'null', respectively. The 'null' category includes images generated through a noise injection process, resulting in normally distributed light pixels placed randomly.
 
 Images drawn from the MNIST dataset have undergone color inversion to ensure consistency throughout the dataset. Vision Transformer Models have been fine-tuned to unify data sourced from varied origins, thereby augmenting the overall accuracy of the dataset. Notably, the 'A-Z handwritten alphabets' dataset, which initially did not distinguish between upper and lower case letters, has been modified to correct this in the present compilation.