5 ms·

Disclaimer: I'm working in the OCR industry, which .. doesn't make me an expert on either state-of-the-art recognition algorithms nor do I know enough about neu

Disclaimer: I'm working in the OCR industry, which .. doesn't make me an expert on either state-of-the-art recognition algorithms nor do I know enough about neural networks to be dangerous. That said: The GP has a point, imo. I don't doubt that the paper describes something new and interesting (and all engines I work with during the day do segmentation/recognize character by character), but localizing a region of interest is usually the hard job for me. When I identify the right region and crop it/scale it/rotate it .. my job's "easy" and I can run a multitude of generally good OCR engines (off the shelf, if you will) and get decent results (maybe vote a bit, use engine A to segment and engine B and C to recognize the characters etc.) So .. ignoring the 'we trained a neural network' part (which makes me nod thoughtfully and mumble 'whatever they did there..'), which I _understand_ is the interesting thing here!, they did more or less what I do all the time. The preceding algorithm is what I do quite a bit less often and which in my environment is more interesting and often challenging. Then again, it can always be labeled as PEBKAC I assume :)

beagle3 13y ago
So ... as someone in the OCR industry, perhaps you could answer: I'm trying to OCR license plates from random videos (stable, but essentially random camera location that sees cars going by and stopping - and I would like to read their plates). I've tried every commercial offering under $4000/license, and -- even when manually localized and a single photo selected, I'm getting less than 95% on a single letter/digit, which translates to ~70% for full plates. (Humans get >99% for full plate on those photos). Where would you recommend I look next for a solution?
- darklajid 13y ago
  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
  - beagle3 13y ago
    Thanks! It's actually Israeli license plates I'm working on right now (because that's the video data set that I got for training so far, though this project is going to be deployed mostly around Europe) - only digits, a standard xx-xxx-xx template, nice standard retro-reflective black-on-yellow - supposed to be the easiest possible case. And it is, for humans. But all the commercial OCRs I managed to find have abysmal performance. When I get some breathing time, I'm going to try the latest Tesseract again (when I last tried, it was v2, and its performance wasn't good).
    - darklajid 13y ago
      If you look at my profile: I got a limited experience with Israel. The OCR company I work for? Sits in Israel.. ;-) I'm reasonably sure that you're not in the market for the things we do, but if you like to chat/want to talk about the project you're doing: Again, feel free to drop me a line. I .. really wouldn't even think about selling things or something, I'm mostly curious about fellow HN users' projects and have an affinity for IL. Good luck!
DannyBee 13y ago
", but localizing a region of interest is usually the hard job for me." On buildings, building numbers tend to stand out dramatically from surroundings. Otherwise, they'd be useless as building numbers :)
- darklajid 13y ago
  Well, the pictures might contain - cars, with numbers (license plate? advertisement, a la "who do you call...") - billboards, with numbers - multiple numbers (some buildings are on street a, 1 and street b, 42) - a lot of noise (garden obstructs most of the house, number isn' that special anymore) Your next task is to find houses.. I don't disagree with you, but you make it sound more easy than it is, in my opinion. Again: Getting a decent start is probably no issue, but competing with human operators...?
  - DannyBee 13y ago
    Sure, I don't disagree at all that it requires a lot of engineering and good algorithms to to compete with human operators. I just think it's significantly more of an "off the shelf" problem than the one addressed by this paper, hence my "isn't the hard part" comment. I did not mean to suggest it is something someone knocks out on a weekend.

beagle3 13y ago

So ... as someone in the OCR industry, perhaps you could answer: I'm trying to OCR license plates from random videos (stable, but essentially random camera location that sees cars going by and stopping - and I would like to read their plates). I've tried every commercial offering under $4000/license, and -- even when manually localized and a single photo selected, I'm getting less than 95% on a single letter/digit, which translates to ~70% for full plates. (Humans get >99% for full plate on those photos). Where would you recommend I look next for a solution?

darklajid 13y ago
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
- beagle3 13y ago
  Thanks! It's actually Israeli license plates I'm working on right now (because that's the video data set that I got for training so far, though this project is going to be deployed mostly around Europe) - only digits, a standard xx-xxx-xx template, nice standard retro-reflective black-on-yellow - supposed to be the easiest possible case. And it is, for humans. But all the commercial OCRs I managed to find have abysmal performance. When I get some breathing time, I'm going to try the latest Tesseract again (when I last tried, it was v2, and its performance wasn't good).
  - darklajid 13y ago
    If you look at my profile: I got a limited experience with Israel. The OCR company I work for? Sits in Israel.. ;-) I'm reasonably sure that you're not in the market for the things we do, but if you like to chat/want to talk about the project you're doing: Again, feel free to drop me a line. I .. really wouldn't even think about selling things or something, I'm mostly curious about fellow HN users' projects and have an affinity for IL. Good luck!

DannyBee 13y ago

", but localizing a region of interest is usually the hard job for me." On buildings, building numbers tend to stand out dramatically from surroundings. Otherwise, they'd be useless as building numbers :)

darklajid 13y ago
Well, the pictures might contain - cars, with numbers (license plate? advertisement, a la "who do you call...") - billboards, with numbers - multiple numbers (some buildings are on street a, 1 and street b, 42) - a lot of noise (garden obstructs most of the house, number isn' that special anymore) Your next task is to find houses.. I don't disagree with you, but you make it sound more easy than it is, in my opinion. Again: Getting a decent start is probably no issue, but competing with human operators...?
- DannyBee 13y ago
  Sure, I don't disagree at all that it requires a lot of engineering and good algorithms to to compete with human operators. I just think it's significantly more of an "off the shelf" problem than the one addressed by this paper, hence my "isn't the hard part" comment. I did not mean to suggest it is something someone knocks out on a weekend.