r/MachineLearning - [D] Using (known) symbolic rules to improve classification/segmentation ?


Using one of your examples, for me the symbolic constraints for OCR of printed source code seem conceptually similar to how standard OCR systems implement the split between the character OCR model (which provides the probabilities of individual characters) and the language model (which provides the probabilities of long combined sequences of possible alternative characters) - any symbolic rules and constraints could be integrated straight into the language model, by adding an extra penalty to the likelihood of sequences that don't match some rule. The algorithms to effectively explore the solution space in this manner (often some form of beam search) already exist and would be already implemented and tested in an OCR system, so the symbolic rules would just change how the cost function is calculated.

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