Hi, i feel confused why upscaling works.Actually, in the tesseract, it also has the process to prescale the image to height 36pix.
在 2018年7月30日星期一 UTC+8下午11:19:23，Emiliano Isaza Villamizar写道： > > Lorenzo, Thank you so much for your help. I did everything step by step > and got a very good result I think what helped me most was up scaling the > images. the code I did is in python and is the following if anyone is > following the thread: > > *import PIL* > *from PIL import Image* > > *im = Image.open(imagepath)* > *hpercent = (baseheight / float(img.size[1]))* > *wsize = int((float(img.size[0]) * float(hpercent)))* > *img = img.resize((wsize, baseheight), PIL.Image.ANTIALIAS)* > > I'm a real newbie in bash so I didn't use your scripts I kept getting a > permission error. Thank you again Lorenzo! > > > > > > > On Thursday, July 26, 2018 at 5:46:44 AM UTC-5, Lorenzo Blz wrote: >> >> First, read this: "Fine Tuning for ± a few characters" >> <https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00#fine-tuning-for--a-few-characters> >> >> >> Then check the data/unicharset file to see if everything is ok, if there >> are all the characters you want. >> >> >> Then, 15000 iterations are way too many and 300 samples are really too >> few. If you train too much you'll get worse results. >> >> I usually get the best fine tuning results from 400 to 2000 iterations. I >> can do more, up to 20k iterations, only when I have many sample images: a >> few thousand with multiple words. >> >> >> I do it like this (this is not a complete guide, just to give you the >> general idea): >> >> - >> clean the data and data/checkpoints folders (do NOT add -rf, you do not >> want to wipe out the training data) >> >> rm data/* >> >> rm data/checkpoints/* >> >> >> (do this only once, when you start a new training session, not after each >> training step) >> >> - >> go into the Makefile and fix this (in the "data/list.eval" block, remove >> the + before $$no): >> >> >> tail -n "$$no" $(ALL_LSTMF) > "$@" >> >> >> then add somewhere at the top: >> >> ITERATIONS=100 >> >> and change the max_iterations line to this (do not change the tabs/spaces >> at the beginning, just replace the number): >> >> --max_iterations $(ITERATIONS) >> >> - now run the training as normal like this: >> >> make training ITERATIONS=100 >> >> - when it finishes run this: >> >> lstmeval --model data/YOUR_MODEL.traineddata --eval_listfile >> data/list.eval >> >> In the last line you'll get something like this: >> >> At iteration 0, stage 0, Eval Char error rate=0.96153846, Word error >> rate=3.8461538 >> >> These are the only values that matter. Take note of these values and the >> iteration numbers. >> >> Make a backup of the model: >> >> cp data/YOUR_MODEL.traineddata data/YOUR_MODEL.traineddata_100 >> >> - Now start the training again with ITERATIONS=200, it will resume from >> the previous iteration up to 200: >> >> make training ITERATIONS=200 >> >> - Run lstmeval again, take note, backup and so on, 300, 400, 500.... >> >> You should see that the error rate will go down for a while then it will >> slow down and then will start to get worse. Use the model where you got the >> best score. >> >> You can try this, but 300 samples are likely way too few for this to be >> meaningful. >> >> I'm attaching my training scripts, they should work but double check >> everything. >> >> >> About thresholding, probably you do not need it, just increase the >> contrast a little, do not go binary. Probably you do not need that either. >> And do the same processing to the training data that you will do on your >> real data. >> >> Two important things, for training and recognition. Use PSM=13 >> (PSM.RAW_LINE). Trim all the white borders, upscale the image so that the >> text is 30-50 pixels tall. >> >> Again, train with the same processing you'll use for recognition. >> >> >> Bye >> >> Lorenzo >> >> >> 2018-07-25 16:49 GMT+02:00 Emiliano Isaza Villamizar <[email protected]>: >> >>> Hello, >>> >>> I'm trying to train tesseract to accurately extract information from a >>> table. Initialy when running with pytesseract I get these results: >>> >>> *pytesseract.image_to_string(img, lang='eng', config='--psm 11 --oem 1 >>> -c tessedit_char_whitelist=0123456789')* >>> >>> I get these results: >>> >>> ground truth Tesseract >>> >>> CN¥6.94 CN#6.94 >>> >>> ¥31660.90 ¥31660.90 >>> >>> Ltd Lid >>> >>> I retrained tesseract with OCR-D, I extracted each cell and wrote the >>> ground truth for 3 tables that add up to 300 cells (300 labeled images). I >>> ran it for 15000 iterations and got an error of 0.5%. But now I get worse >>> results. Tesseract doesn't seem to read numbers and basic acronyms.attached >>> you may find an example of an image used for training. >>> >>> ground truth New tesseract >>> >>> 000426.China ooo426.cin >>> >>> How can I improve tesseract to read these weird characters? I already >>> tried to improve the image quality by transforming the image using CV2 this >>> is an example: >>> >>> >>> th3 = >>> cv2.adaptiveThreshold(img_grey,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2) >>> >>> img_grey = cv2.cvtColor(atable, cv2.COLOR_BGR2GRAY) >>> >>> >>> Thanks!! >>> >>> -- >>> 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/1b05ace0-4ca6-4caf-94a8-d53f7c0bec35%40googlegroups.com >>> >>> <https://groups.google.com/d/msgid/tesseract-ocr/1b05ace0-4ca6-4caf-94a8-d53f7c0bec35%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. 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/09043939-0f2b-45e7-9f54-130eb8d03299%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
