Hard to tell. First: I'm a lowly developer here, right? I can't and won't sell you stuff. In addition: While I care a lot about my craft/developing, I don't car

Hard to tell. First: I'm a lowly developer here, right? I can't and won't sell you stuff. In addition: While I care a lot about my craft/developing, I don't care about the industry - I'm blind to most competition. Regarding your particular problem: No idea about international license plates (or the one you are interested in). It helps a lot to restrict engines to character sets. German license plates are roughly [1] ([A-Z]{1,3})-([A-Z]{1,2})(\d{1,4}) which helps a lot. Usually you try to combine 'dumb' OCR with datasets/fuzzy matches to rule out errors. Only you know if that is possible for your dataset. Depending on whether I understood your problem correctly we might again have the localization issue (which I complained about above): Find the licence plate, crop and rotate it. Bonus points if your images might contain multiple license plates and a human operator would 'obviously' see the right one.. Recognition itself should be okay: Limited character set, a limited number of fonts (here: One only) and hopefully decent binarization opportunities (here: black on white, background reflective). Feel free to shoot me a mail, details in my profile. 1: From memory, might be slighly inaccurate, sample only