Pytesseract OCR multiple config options
The reason you are having trouble is because character restriction does not work in version 4.0. You have to force legacy mode (oem 0) to have it limit found characters. There is a bug somewhere in the tesseract team that they have not yet addressed.
Tesseract version 5.0.0-alpha can use the following command: (use psm=13 and oem=1 or 3) pytesseract.image_to_string(export_image ,lang='eng', config='--psm 13 --oem 1 -c tessedit_char_whitelist=ABCDEFG0123456789')
Note that eng
trained dataset is taken: https://github.com/tesseract-ocr/tessdata_fast/blob/master/eng.traineddata
Note:Tested on binary input images of +-60x60px with single character
Page segmentation modes:
Orientation and script detection (OSD) only.
Automatic page segmentation with OSD.
Automatic page segmentation, but no OSD, or OCR. (not implemented)
Fully automatic page segmentation, but no OSD. (Default)
Assume a single column of text of variable sizes.
Assume a single uniform block of vertically aligned text.
Assume a single uniform block of text.
Treat the image as a single text line.
Treat the image as a single word.
Treat the image as a single word in a circle.
Treat the image as a single character.
Sparse text. Find as much text as possible in no particular order.
Sparse text with OSD.
Raw line. Treat the image as a single text line, bypassing hacks that are Tesseract-specific.
OCR Engine modes:
- Legacy engine only.
- Neural nets LSTM engine only.
- Legacy + LSTM engines.
- Default, based on what is available.
tesseract-4.0.0a
supports below psm
. If you want to have single character recognition, set psm = 10
. And if your text consists of numbers only, you can set tessedit_char_whitelist=0123456789
.
Page segmentation modes:
0 Orientation and script detection (OSD) only.
1 Automatic page segmentation with OSD.
2 Automatic page segmentation, but no OSD, or OCR.
3 Fully automatic page segmentation, but no OSD. (Default)
4 Assume a single column of text of variable sizes.
5 Assume a single uniform block of vertically aligned text.
6 Assume a single uniform block of text.
7 Treat the image as a single text line.
8 Treat the image as a single word.
9 Treat the image as a single word in a circle.
10 Treat the image as a single character.
11 Sparse text. Find as much text as possible in no particular order.
12 Sparse text with OSD.
13 Raw line. Treat the image as a single text line,
bypassing hacks that are Tesseract-specific.
Here is a sample usage of image_to_string
with multiple parameters.
target = pytesseract.image_to_string(image, lang='eng', boxes=False, \
config='--psm 10 --oem 3 -c tessedit_char_whitelist=0123456789')
Hope this helps.