Braille Character Recognition (BCR) is a method to locate and recognize Braille document stored as an image, and convert the braille in image into equivalent natural language representation. BCR converts the pixel representation of an image into its equivalent character representation. Based on literature review studies and remarks it can be concluded that extracting information from braille paper requires accuracy in preprocessing stage and then the processed image is mapped into an artificial neural network in order for optimal detection. The system was tested with a variety of Braille documents written using English Braille standards. Digital image processing stages like Gray scale conversion, binary conversion; filtering and morphological operations have been applied in the preprocessing stage which results in enhanced quality of Braille dots. Furthermore, image segmentation, image cropping and resizing has been applied to the braille document in order to improve the matching method. The proposed method in this project extracts the braille dots from the braille picture, and then maps the image pixels into the machine which has been trained with the braille data training set using back propagation algorithm. And then transform the character into English text. The implemented algorithm achieved an average of 89.62% while taking 200 hidden nodes and a learning rate of 0.3 precise results when several cases have been performed with excellent recognition outcomes.