Timeline for TextRecognize with crosses, circles and spaces results into oddities, why?
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
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| May 16, 2013 at 9:12 | comment | added | image_doctor | This approach is based on measuring the distance between a sample and a prototype or template, its success or failure depends on the closeness of those two things. As a generalised problem, text recognition is a sub class of machine learning ( aka pattern recognition). There are a broad range of more complex techniques that might be applied here, maybe what you are seeking depends on whether you want to learn in more depth how those things work or if you just need a solution to your immediate problem. | |
| May 16, 2013 at 7:34 | comment | added | image_doctor | There are a number of reasons this doesn't give the result you would like. Firstly have you had a look at the structure of grid, particularly what grid[[1]] might look like. Similarity takes a single image as it's first input, not a list of images. Secondly the second parameter is intended to be a set of prototype images which have exactly the same image dimensions as, and a strong visual resemblance to, the images you wish to recognise and a set of labels to associate with those prototypes. | |
| May 16, 2013 at 1:11 | comment | added | hhh | I tried to do it for the grid but img = Import["https://i.sstatic.net/NbTGY.jpg"]; grid = ImagePartition[ ImageCrop[ImageRotate[img, 0.7 \[Degree]], {1180, 720}, {-0.15, 0.2}], {118, 119}]; Table[similarity[i, {{x, o, empty}, {"x", "o", " "}}], {i, grid}] -- traversing not working correctly?! Wait it does not work because this method works only with specific cross and specific circle? | |
| May 15, 2013 at 21:43 | history | answered | image_doctor | CC BY-SA 3.0 |