![]() For an Optical Character Recognition (OCR) algorithm to accurately read such visual data, it must be able to do the same.* The mind accounts for distortions, scaling, and other stylistic differences to associate two character sets. The human brain identifies characters based on its relative shape. To the human observer, however, it is fairly easy to read. Such an algorithm would perform poorly since the shapes are mismatched. Imagine an algorithm designed to compare each handwritten character pixel-by-pixel to an identical printed character. Suppose you have handwritten notes that you would like to digitize. ![]() I ultimately chose to implement a Shape Context method to identify characters based on their relative shapes. To explore the current approaches in OCR algorithms by performing a literature survey and then writing my own code using a selected technique. Remember to specify a name for the training data file by editing the OcrDefaults settings. To train the program with new data, execute TemplateTraining in the Matlab command window. To run the program with default settings, execute ocr in the Matlab command window. ![]() To run the program with a GUI, execute OcrProgram in the Matlab command window. Modify the default settings in Data/OcrDefaults.m. These are then used to measure similarities between shapes and to recognize a character based on each edge’s polar-log distance to the other edges.Īdd the Matlab OCR folder to Matlab's path. This gives a globally discriminative characterization of the shape and not just a localized descriptor. ![]() The Shape Context is a shape descriptor that captures the relative positions of other points on the shape contours. Shape Context - Optical Character Recognition (OCR) ![]()
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