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Editorial Team
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Asked: 2026-06-10T06:09:15+00:00

I want to implement a OCR system. I need my program to not make

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I want to implement a OCR system. I need my program to not make any mistakes on the letters it does choose to recognize. It doesn’t matter if it cannot recognize a lot of them (i.e high precision even with a low recall is Okay).

Can someone help me choose a suitable ML algorithm for this. I’ve been looking around and find some confusing things. For example, I found contradicting statements about SVM. In the scikits learn docs, it was mentioned that we cannot get probability estimates for SVM. Whereas, I found another post that says it is possible to do this in WEKA.

Anyway, I am looking for a machine learning algorithm that best suites this purpose. It would be great if you could suggest a library for the algorithm as well. I prefer Python based solutions, but I am OK to work with Java as well.

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1 Answer

  1. Editorial Team

    It is possible to get probability estimates from SVMs in scikit-learn by simply setting probability=True when constructing the SVC object. The docs only warn that the probability estimates might not be very good.

    The quintessential probabilistic classifier is logistic regression, so you might give that a try. Note that LR is a linear model though, unlike SVMs which can learn complicated non-linear decision boundaries by using kernels.

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