Acctually, the accuracy of the OCR is hard to be guaranteed. As i know you may select a smaller region from mGray where your text is, before createBitmap - so the more heavy methods that follow process a smaller image.
On Tuesday, March 29, 2011 at 12:17:47 PM UTC+8, Andres wrote: > > ...required. > > Hello people, > > I'm develping a licence plate recognition system from long ago and I still > have to improve the use of Tesseract > <http://www.myknown.com/ocr/tesseract-ocr-engine/> to make it usable. > > My first concern is about speed: > After extracting the licence plate image, I get an image like this: > > > https://docs.google.com/leaf?id=0BxkuvS_LuBAzNmRkODhkYTUtNjcyYS00Nzg5LWE0ZDItNWM4YjRkYzhjYTFh&hl=en&authkey=CP-6tsgP > > As you may see, there are only 6 characters (tess is recognizing more > because there are some blemishes over there, but I get rid of them with > some postprocessing of the layout of the recognized chars) > > In an Intel I7 720 (good power, but using a single thread) the tesseract > part is taking something like 230 ms. This is too much time for what I need. > > The image is 500 x 117 pixels. I noted that when I reduce the size of this > image the detection time is reduced in proportion with the image area, > which makes good sense. But the accuracy of the OCR > <http://www.myknown.com/ocr/optimization/> is poor when the characters > height is below 90 pixels. > > So, I assume that there is a problem with the way I trained tesseract. > > Because the characters in the plates are assorted (3 alphanumeric, 3 > numeric) I trained it with just a single image with all the letters in the > alphabet. I saw that you suggest large training but I imagine that that > doesn't apply here where the characters are not organized in words. Am I > correct with this ? > > So, for you to see, this is the image with what I trained Tesseract: > > > https://docs.google.com/viewer?a=v&pid=explorer&chrome=true&srcid=0BxkuvS_LuBAzODc1YjIxNWUtNzIxMS00Yjg3LTljMDctNDkyZGIxZWM4YWVm&hl=en&authkey=CMXwo-AL > > In this image the characters are about 55 pixels height. > > Then, for frequent_word_list and words_list I included a single entry for > each character, I mean, something starting with this: > > A > B > C > D > ... > > Do you see something to be improved on what I did ? Should I perhaps use a > training image with more letters, with more combinations ? Will that help > somehow ? > > Should I include in the same image a copy the same character set but with > smaller size ? In that way, will I be able to pass Tesseract smaller images > and get more speed without sacrificing detection quality ? > > > On the other hand, I found some strange behavior of Tesseract about which > I would like to know a little more: > In my preprocessing I tried Otsu thresholding ( > http://en.wikipedia.org/wiki/Otsu%27s_method) and I visually got too much > better results, but surprisingly for Tesseract it was worse. It decreased > the thickness of the draw of the chars, and the chars I used to train > Tesseract were bolder. So, Tesseract matches the "boldness" of the > characters ? Should I train Tesseract with different levels of boldness ? > > I'm using Tesseract 2.04 for this. Do you think that some of these issues > will go better by using Tess 3.0 ? > > > Thanks, > > Andres > > > > > > > -- 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 https://groups.google.com/group/tesseract-ocr. To view this discussion on the web visit https://groups.google.com/d/msgid/tesseract-ocr/41a8c2fc-9533-4375-925d-71663057a882%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
