Context During the time of developing an OCR software, this dataset was used to train the neural network. The original dataset has 1.2M images and this dataset is a random sample of 10k images from the main dataset. Content This dataset is a mix bag of images gathered from multiple sources. 1. Manually cropped and labelled images from natural scanned documents. 2. Synthetically generated images which look very similar to natural images to boost infrequent characters. 3. Data labelled using [tesseract OCR][1] software and manually checked for OCR errors. Preview ![IT][2] ![oftener][3] ![check][4] ![Spor][5] ![she>][6] ![smirking][7] ![for][8] ![(2)][9] # Details Images are in raw format and do not have a specific size. Images may need to be resized for training. Only jpeg and png images. Characters vocabulary: English characters small/capital and special symbols Acknowledgements This dataset would not have been possible without the contributions of all the manual labellers, data contributors and also the developers of [tesseract OCR][10] software which was used to label a portion of this dataset. Inspiration This data is portrayed as a successor of the very famous MNIST dataset. This is done to give a more challenging task to the beginners who have solved the MNIST dataset and are looking for a level 2. [1]: https://github.com/tesseract-ocr/tesseract/ [2]: https://image.ibb.co/g3ZLTT/12.jpg [3]: https://image.ibb.co/gnN78T/23.jpg [4]: https://image.ibb.co/kHVS8T/49.png [5]: https://image.ibb.co/iFpLTT/75.jpg [6]: https://image.ibb.co/gMcUNo/104.jpg [7]: https://image.ibb.co/mrSUNo/116.jpg [8]: https://image.ibb.co/eKUyF8/135.jpg [9]: https://image.ibb.co/g8MOho/188.png [10]: https://github.com/tesseract-ocr/tesseract/