what is the possible solution for the Intra-class variations poblems?
It boils down to the features.  You have shown some images of the same class. You also need to look at images of the other class(s), and find a set of features that maximize inter-class variance and minimize intra-class variance. What features those might be depends on your data. You need to look at the types of variation that exists within each class, and try to find features, transformations, or distance measures that account for it. It is very hard to tell you anything more specific based on the information you have provided.

Edit:
One thing you can do is post some images of a different class.  

Also, are the images aligned? Maybe before you compute the features you should translate all your images so that the white pixels coincide as much as possible.  Then you can define a distance measure between two images as the percentage of white pixels that co-locate, and then use a k-nearest-neighbor classifier.

Some other random thoughts:
Shape context features may be suitable for your problem.

You also might want to search the literature on recognizing hand-written characters.