Recognition and classification of alphanumeric characters from image or pdf files.

1. python 3.5.2
2. matplotlib 1.5.3
3. pandas 0.18.1
4. numpy 1.11.1
5. SimpleITK 0.10.0
6. scipy 0.18.1

1. Run "python generatefeatureset.py" (set the variables "datafolder", "featuresfolder" and "outputfile" accordingly before running).
2. Run "python crossvalidate.py" to get the crossvalidation results.

• Characters 0-9 of Char74k data set is used for training the data.

• The images are first converted into grayscale and binary thresholded to convert them into black and white images.
• The images are cropped with necessary zero padding, so that they are contained in a square.
• All the images are resized to a single size of 100x100.

• A 100x100 image is sub-divided into 5 parts horizontally.
• For each part of the sub-divided image, GLCM features with different combinations of angles (0, 45, 90, 135) and distances (1, 3, 5) are extracted.
• Extracted features are stored in an excel file.

• A five fold cross validation technique is used to classify the images in the dataset.
• SVM classifier with regularization parameter, C = 1000 with 'rbf' kernel is used to classify the images.

• The cross validation accuracy for digits 0-9 is 84.6%.