It sort of depends on your hardware and how similar or different they are. Reliable feature matching works on distinct features (so there need to be enough points of interest (edges usually) that cover text, buttons, other bits and pieces). If, for example, all your hardware was the same as the example you originally posted and only the model number was changing then this would be an issue most likely as the feature matching may match several targets.
Also you mention the tech takes a picture on mobile. Does that need to be looked up immediately? The issue is that feature matching is CPU heavy and can take time on mobile and is a function of the photo resolution. Luckily, feature matching appears to work better on lower resolution images and most of the time works in black and white. Then there is the potential number of hardware items you are trying to match. The most advanced mobile augmented reality products (Metaio, Vuforia) that use feature matching only allow up to 100 targets to be "tracked" or "looked for" at a time - every piece of hardware you are looking for needs to be compared to the live input camera view (or photo) and this is the part that hits the CPU hard. If however there was an option to offload the image(s) to a backend cloud server for feature match or if the tech did not need an instant or any kind of result in the field, then you are in a better situation as you can stand up serious computing power. It's not easy to recommend one or the other without all the facts - as you begin to mention new things like mobile and techs in the field, this changes things :) For instance I also used mobile - an Android tablet, with Open CV and Tesseract OCR - the combination worked in the field - the tech can position the camera face-on to the model number and take a close photo. You could even provide a mini App for your techs that has a basic cropping tool. The technique I used was to show the camera view in my app with a little white transparent box over the camera view that allowed the user to position the text to fit that white box. Then, when the photo was taken I simply cropped that white box coordinate rectangle and I had a perfect match. This was easy vs. feature matching :) On Wednesday, 7 January 2015 23:02:09 UTC, newbie wrote: > > Sorry for the barrage here. > The interesting thing is you mentioned feature matching with openCV(I dont > know anything at all about it). But the one thing is I can have a > repository of these images with me and I need to match it to one of the > user generated image. > > A little background might help. I can(or come up with) have a repository > of all the equipment images with me. A tech might head to the field, take a > picture on his mobile device and I need to match it(tech's picture) > against my repository and come up with the model number. > > Is this easier with ocr or feature matching with openCV ? > > Thanks > > On Wednesday, January 7, 2015 5:35:47 PM UTC-5, newbie wrote: >> >> Thanks Allistair , my lucky day as you have responded to both my queries. >> Let me try to address your questions below and then go ahead with a few of >> my own :-) >> >> *I also meant to ask whether your use case allows for cropping. If you >> know you will have a certain format of image, cropping an area and >> resampling should be easy.* >> Basically the image will be an user generated image, more like the first >> png file, but we could ask the user to zoom in to the model number, if that >> would help us indentify the model number.we could do anything with the >> image(cropping ,resampling etc). But the problem is the model number >> probably will not be located at the same place for all equipments. >> >> 2. Preprocessing - as it should be programatically done would I be using >> opencv in conjunction with tesseract? I did not see much in tesseract for >> image processing(I could be totally off). >> 3.*.I also use psm 6 for these types of image with various text >> locations.* >> what is this ? >> >> Another thing I probably can come up with is all the model #s or images >> of all potential equipments, so I have repository to match against. Would >> that help in any way ? >> >> Thanks again for taking the time to respond. Appreciate it. >> >> >> >> On Wednesday, January 7, 2015 4:44:47 PM UTC-5, Allistair C wrote: >>> >>> I also meant to ask whether your use case allows for cropping. If you >>> know you will have a certain format of image, cropping an area and >>> resampling should be easy. You could also do some preprocessing that looks >>> for certain icons in your image to get some context as to where the model >>> number is likely to be (see feature matching on Open CV). However, I would >>> need to know more about your use case. >>> >>> That said, resampling your full image to 3000px wide yielded a result >>> with a full model number but the more you can crop the area the better the >>> result: >>> >>> AT&T U verse ‘ § >>> LINK HD nzc , >>> rowzn Q I ‘ .» . ‘ nsuu 4 0|: > I >>> / sj J \ >>> VIP2500 °%' 7 A R R I s >>> >>> >>> On 7 January 2015 at 21:39, Allistair <[email protected]> wrote: >>> >>>> A common technique is to pre-process your input image. >>>> >>>> Resizing produced good results.I also use psm 6 for these types of >>>> image with various text locations. >>>> >>>> In this case I first used your cropped image: >>>> >>>> tesseract ArrisVIP2500_cropped.png out -l eng -psm 6 config >>>> >>>> and got: >>>> >>>> AT&T U verse >>>> rowsn >>>> O F3. >>>> vrrzsoo ’e' >>>> >>>> Then I resampled your image to 2000px wide: >>>> >>>> tesseract ArrisVIP2500_cropped_2000.png out2000 -l eng -psm 6 config >>>> >>>> and got: >>>> >>>> AT&T U verse >>>> POWER © " ‘| >>>> / ‘j""'j"’.. >>>> VIP2500 '%’ >>>> >>>> Cheers >>>> >>>> >>>> >>>> On 7 January 2015 at 19:26, newbie <[email protected]> wrote: >>>> >>>>> I am using tess4j, a java wrapper around tesseract and Here are the >>>>> images and results. The intent is to extract VIP2500(model number) from >>>>> the >>>>> image. An help is appreciated. >>>>> >>>>> Attached are the original png file ( ArrisVIP2500.png),binarized >>>>> file(ArrisVIP2500_bin.TIF) and then a zoomed and cropped >>>>> file(ArrisVIP2500_cropped.png). >>>>> >>>>> *ArrisVIP2500.png* >>>>> >>>>> é ATE-T U-verse >>>>> >>>>> rowan 0 >>>>> / >>>>> >>>>> *ArrisVIP2500_bin.TIF* >>>>> >>>>> AT&T U-verse >>>>> >>>>> rowan <3 3 >>>>> / -- >>>>> >>>>> vxvzsoo ‘Q’ >>>>> >>>>> *ArrisVIP2500_cropped.png* >>>>> >>>>> ATE-T U-verse >>>>> >>>>> rowsn Q >>>>> >>>>> VIPZSOO ‘e’ This looks the closest to VIP2500 , I >>>>> need to get tess4j to reconginze digits, that said, this might not be a >>>>> realistic scenario, as someone/something >>>>> >>>>> Needs to zoom and crop the >>>>> image before hand(preprocessing). >>>>> >>>>> -- >>>>> You received this message because you are subscribed to the Google >>>>> Groups "tesseract-ocr" group. >>>>> To unsubscribe from this group and stop receiving emails from it, send >>>>> an email to [email protected]. >>>>> To post to this group, send email to [email protected]. >>>>> Visit this group at http://groups.google.com/group/tesseract-ocr. >>>>> To view this discussion on the web visit >>>>> https://groups.google.com/d/msgid/tesseract-ocr/009ffbc7-90cc-417a-90c8-b4ac9b5bb203%40googlegroups.com >>>>> >>>>> <https://groups.google.com/d/msgid/tesseract-ocr/009ffbc7-90cc-417a-90c8-b4ac9b5bb203%40googlegroups.com?utm_medium=email&utm_source=footer> >>>>> . >>>>> For more options, visit https://groups.google.com/d/optout. >>>>> >>>> >>>> >>> -- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. 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