Thanks a lot for your answer !
After playing around, the issue is that apparently both whitelist and
blacklist aren't supported in this scenario and make tesseract return
nothing, but i don't really understand why because it works find in another
scenario (for whole picture recognition, before slicing into smaller parts).
Regarding documentation, i have big troubles to find informations on
tesseract-ocr.github.io or in the github doc about theses two options and *how
they behave when put together.*
Maybe it's in a corner or a detail i missed, anyway, if anyone stumble on
this topic in the future it might be helpful to better reference it in the
doc.
Beside the char types definition, i don't find much about it :
https://tesseract-ocr.github.io/tessapi/3.x/a00624.html#aba81894cd2dc9f32e71da97cabad5580
Sorry if it sounds a bit dumb, but again, i'm a newbie on OCR and image
recognition, and i like newbie friendly tools ;)
Le mercredi 14 février 2024 à 07:02:36 UTC+1, zdenop a écrit :
> Works like a charm: just read and follow documentation carefully:
>
> >tesseract e_I_read_documetation_carefully.png - --psm 10
> D
> >tesseract d_I_read_documetation_carefully.png - --psm 10
> E
> >tesseract d-I_read_documetation_carefully.png - --psm 10
> D-
>
>
> Zdenko
>
>
> st 14. 2. 2024 o 2:14 dev 313153 <dev3...@gmail.com> napísal(a):
>
>> Hello,
>> I managed to implement a dynamic parsing to get rid of OSD issues i had.
>> However i'm blocking on recognizing single uppercase letter, i tried many
>> different configurations for preprocessing but i can't get to find the
>> right one, even with PSM set to 10, i don't really know what i could try.
>> Any help is appreciated.
>>
>> Here is code snippet for testing with pictures attached :
>> import cv2
>> import os
>> import pytesseract
>> import numpy as np
>>
>> pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR
>> \tesseract.exe'
>>
>> for pic in ["e.png","d-.png","d.png"]:
>> img=cv2.imread(pic)
>>
>> #Preprocessing
>> img = cv2.resize(img, (70, 90), interpolation=cv2.INTER_NEAREST)
>> norm_img = np.zeros((img.shape[0], img.shape[1]))
>> img = cv2.normalize(img, norm_img, 0, 255, cv2.NORM_MINMAX)
>> img = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 15)
>> img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
>> img = cv2.bitwise_not(img)
>> img = cv2.threshold(img,127,255,cv2.THRESH_BINARY) [1]
>> cv2.imwrite("processed-"+pic, img)
>>
>> # Tesseract OCR
>> text = pytesseract.image_to_string(img, lang='eng', config='-c
>> tessedit_char_whitelist=\\ ABCDEF+- tessedit_char_blacklist=\\=!,*%^$°:.
>> --psm 10 -oem 3')
>> print(str(text).replace("\n", " "))
>>
>>
>> Le mercredi 7 février 2024 à 06:39:37 UTC+1, dev 313153 a écrit :
>>
>>> Hello,
>>> I am very new to tesseract, as well as in image processing in general.
>>> I have screenshots from which i want to extract text for further
>>> processing, i played around with tesseract after checking the Improve
>>> Quality URL and was able to extract what i need (most of the time).
>>> For example, in attached screenshots, i want to extract names of the
>>> stats and the following letter together, but it doesn't always work.
>>> Sometime the letter isn't extracted, and sometime it is, but the OSD
>>> consider it belongs on an other level or row and it's output ahead or
>>> before the stats names when i use image_to_string.
>>> I also tried to play with oem and psm settings, without much
>>> improvements.
>>>
>>> I attached some example of image_to_string outputs for different
>>> pictures as well as images and the python code i'm using as testing bench.
>>>
>>> I am getting a bit desesperate, so i consider the following approaches :
>>> - training my own dataset for this need, having sufficient data
>>> shouldn't be an issue over time but i have zero experience on this kind of
>>> thing.
>>> - looking for the stats names coordinates, and then cropping the picture
>>> around it to make sure tesseract focusses on it and extract it properly
>>> (sounds like a chore code wise, but doable i think).
>>>
>>> Let me know what you think about it or if you have a improvements to
>>> suggest.
>>> Best Regards,
>>>
>>> --
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