get the zone detection in the order

[Jambon1510]

current behavior Some of image which needs to be detected have zone detection appearing in different orders than what we see (from left to right and top to bottom) Let's take this example below

The number 3 will arrive in the results before 7 whereas it should appear before 6

OCRed text: q85920q61437

When I try the grounding on this particular picture we can indeed see that 3 detection comes in the same order as the result above

expected behavior Have the below result

OCRed text: q85920q36147

[goncalopp]

I'm not sure where the "q" are coming from, but in terms of the order of segments you might have to tune the parameters in SegmentOrderer ({"max_line_height": 20, "max_line_width": 10000})

Can you please attach the code you're using for the OCR and grounding and the commit you're at?

[Jambon1510]

I tried tuning max_line_height without success

The code I am using is the latest commit (5e29b25) and I am using both example_grounding.py and example.py (I have only renamed the file inside it)

PS: I have just put a 'q' for the unknown char, I have realized later than I should put '<' (I can't see the instruction as I have heaps of below message I still haven't managed to fix

QObject::moveToThread: Current thread (0x1066ec0) is not the object's thread (0x124c8e0).
Cannot move to target thread (0x1066ec0)
```)

[goncalopp]

I've used the image you attached in this thread, and the code on 5e29b25a22732c146155ee1f49c255b5e0b5e6ee, and using example_grounding.py and example.py (unchanged except for the image filename)and I'm getting different results:

In this case, it mostly works. The 5 and the 2 are getting vertically split, but is likely fixable by increasing blur, or doing a dilate operation.

I'm guessing this is not similar to your case though? What environment are you running this on? (versions for OS, python, opencv and numpy) Do you mind attaching screenshots of your segmentation steps?

[Jambon1510]

I'm guessing this is not similar to your case though?

Indeed (cf screenshots below)

What environment are you running this on? (versions for OS, python, opencv and numpy)

OS: _lsbrelease -a No LSB modules are available. Distributor ID: Ubuntu Description: Ubuntu 18.04.1 LTS Release: 18.04 Codename: bionic

python version module: python3 --version Python 3.6.7

python version module: pip3 freeze | grep -E 'opencv|numpy' numpy==1.15.4 numpydoc==0.7.0 opencv-python==3.4.4.19

Do you mind attaching screenshots of your segmentation steps?

Sure, here you go:

[goncalopp]

Thanks! I'm going to install your Ubuntu version and give it a try In the meantime, if you want to take a look to see if everything is working as intended, you might want to run the unit tests with python setup.py test, and python3 as well

[Jambon1510]

Thanks a lot for your help, let me know what are your results with similar configuration.

I have one failure as below when running the unit tests

python3 setup.py test
running test
running egg_info
writing simpleocr.egg-info/PKG-INFO
writing dependency_links to simpleocr.egg-info/dependency_links.txt
writing requirements to simpleocr.egg-info/requires.txt
writing top-level names to simpleocr.egg-info/top_level.txt
reading manifest file 'simpleocr.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching 'data' under directory 'simpleocr'
writing manifest file 'simpleocr.egg-info/SOURCES.txt'
running build_ext
test_ground (tests.test_files.TestImageFile) ... ok
test_ground_unicode (tests.test_files.TestImageFile) ... ok
test_open_image (tests.test_files.TestImageFile) ... ok
test_open_image_nonexistent (tests.test_files.TestImageFile) ... ok
test_terminal_grounder (tests.test_grounding.TestGrounding) ... Found 125 segments to ground.
Type 'exit' to stop grounding the file.
Type ' ' for anything that is not a character.
Grounding will exit automatically after all segments.
Going back to a previous segment is not possible at this time.
ok
test_textgrounder (tests.test_grounding.TestGrounding) ... ok
test_textgrounder_wrong_len (tests.test_grounding.TestGrounding) ... ok
test_usergrounder (tests.test_grounding.TestGrounding) ... For each shown segment, please write the character that it represents, or spacebar if it's not a character. To undo a classification, press backspace. Press ESC when completed, arrow keys to move
showing segment 0 (waiting for input)
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classified  125 characters out of 125
ok
test_ocr_digits (tests.test_ocr.TestOCR) ... 314159265358979323846264338327950288419716939937510582097494459230781640628620899862803482534211706798214808651328230664709384460955058223172535940812848111745028410270193852110555964462294895493038196442881097566593344612847
314159265358979323846264338327950288419716939937510582097494459230781640628620899862803482534211706798214808651328230664709384460955058223172535940812848111745028410270193852110555964462294895493038196442881097566593344612847
ok
test_ocr_unicode (tests.test_ocr.TestOCR) ... ᚠᛇðþპηγსλσαлльԿմгրяვرეնიىඑயաçයæනγɐດɜຍ我下而身əᕆœ€ᔭ
ᚠᛇðþηγλσαдльгяვეპისயçæɐɜԿրնամرىඑයනດຍ我下而身ᕆᔭəœ€
FAIL
test_opencv_brightness (tests.test_opencv_utils.TestOpenCVUtils) ... ok
test_opencv_brightness_raise (tests.test_opencv_utils.TestOpenCVUtils) ... ok
test_opencv_imageprocesser (tests.test_opencv_utils.TestOpenCVUtils) ... ok

======================================================================
FAIL: test_ocr_unicode (tests.test_ocr.TestOCR)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/mnt/simple-ocr-opencv-master/tests/test_ocr.py", line 31, in test_ocr_unicode
    self._test_ocr(open_image('unicode1'), open_image('unicode1'))
  File "/mnt/simple-ocr-opencv-master/tests/test_ocr.py", line 24, in _test_ocr
    self.assertEqual(chars, reconstruct_chars(ground_truth))
AssertionError: 'ᚠᛇðþპηγსλσαлльԿմгրяვرეնიىඑயաçයæනγɐດɜຍ我下而身əᕆœ€ᔭ' != 'ᚠᛇðþηγλσαдльгяვეპისயçæɐɜԿրնամرىඑයනດຍ我下而身ᕆᔭəœ€'
- ᚠᛇðþპηγსλσαлльԿմгրяვرეնიىඑயաçයæනγɐດɜຍ我下而身əᕆœ€ᔭ
+ ᚠᛇðþηγλσαдльгяვეპისயçæɐɜԿրնամرىඑයනດຍ我下而身ᕆᔭəœ€

----------------------------------------------------------------------
Ran 13 tests in 3.614s

FAILED (failures=1)
Test failed: <unittest.runner.TextTestResult run=13 errors=0 failures=1>
error: Test failed: <unittest.runner.TextTestResult run=13 errors=0 failures=1>

[Jambon1510]

Hi Any chance to reproduce the issue I am encountering?

[goncalopp]

Hi @Jambon1510 Sorry, for the the delay, I'm a bit short on time lately I've reproduced the issue in Ubuntu 18.04 using python2. I get the exact same results as you, for the segmentation and the tests. I haven't had time to dig into the cause yet

Funnily enough, the provided example image (for pi) works well, and the unicode one seems to only have trivial errors. What's the copyright status of your image? Is it something that we could include in this repository to develop more tests and prevent future regressions?

[Jambon1510]

Thanks a lot for your time Weirdly on my side I can't get the result I had in the first place. I have now below output which is still incorrect:

OCRed text:
 <<3865194207

same if I tried to ground again with 'q' as unknown character as I did before (I will just have a 'q' instead of '<' in the output) Also I check again the versions and they are the same as I put in the comment on the 27 of December 2018.

On my Pi3, same result as above but versions seems to be a bit outdated (I will update and try again)

pi@raspberrypi3 ~ $ lsb_release -a
No LSB modules are available.
Distributor ID: Raspbian
Description:    Raspbian GNU/Linux 9.4 (stretch)
Release:    9.4
Codename:   stretch

pi@raspberrypi3 ~ $ python3 --version
Python 3.5.3

pi@raspberrypi3 ~ $ sudo pip3 freeze | grep -E 'opencv|numpy'
numpy==1.12.1
opencv-python==3.4.4.19

For the picture and to be honest with you it is taken from this website so not sure about the copyright

[goncalopp]

So, I was experimenting with this a bit more, and I think the reason the order and line finder are getting confused is just because they're detecting the edges of the background. The algorithm doesn't really do any form of background removal, so you'll have to script that out if you're interested in doing this kind of image.

Using simple thresholding, I get this:

Which works as you would expect, in of character detection and line detection. I've done this in a image processor, but you can, of course, just do the thresholding in OpenCV. Take a look at this code https://github.com/goncalopp/simple-ocr-opencv/blob/master/simpleocr/segmentation.py#L57

I hope that works for you, let me know how it goes!

[Jambon1510]

Ok thanks a lot Do you mind helping me to understand how to use your code to apply the image processing? I am trying to use your code by changing in ocr.py

below

 class OCR(object):
     def __init__(self, segmenter=None, extractor=None, classifier=None, grounder=None):
         self.segmenter = get_instance_from(segmenter, SEGMENTERS, "contour")

to

 class OCR(object):
     def __init__(self, segmenter=None, extractor=None, classifier=None, grounder=None):
         self.segmenter = get_instance_from(segmenter, SEGMENTERS, "rawcontour")

but same result, I guess I am doing something wrong

[goncalopp]

Are you sure the result is the same?

If you check here: https://github.com/goncalopp/simple-ocr-opencv/blob/8611fc9e1054033b53478186f072497647b6be63/simpleocr/segmentation.py#L82

CountourSegmenter does a few more things than the raw one - blurring the original image and ordering the outputed segments.

In any case, you should do the image processing outside of this project code - on your own program (something similar to example.py), using cv2.adaptiveThreshold . The harder part is extracting the image - I'd advise you to drop into the REPL (you can use import pdb; pdb.set_trace() and inspect the contents of the image object (test_image, for example)

[Jambon1510]

Ok I have only managed to make it black and white but the order is still not respected.

I have then decided to proceed to the horizontalization of the image as below

When grounding I had an issue on line numbers I am not facing anymore for an unknown reason. Anyway I am now able to get this in the order when I try with below example (even if I am not sure that removing contour or applying a contrast filter is necessary with the horizontalization, need to give a try)

Result: OCRed text: 3051792684

Youhou! Thanks a lot for your help

Pre-processing piece of code on horizontalization for those interested (inspired by dermen post)

import cv2
import numpy as np
from PIL import Image
import PIL

image = cv2.imread('p19.png')
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

image = cv2.adaptiveThreshold(image, maxValue=255, adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
    thresholdType=cv2.THRESH_BINARY, blockSize=11, C=10)

cv2.imwrite("nocontour.png", image)

image = Image.open("nocontour.png")

area = (0,0,173,32)
cropped_img1 = image.crop(area)

area = (0,33,173,65)
cropped_img2 = image.crop(area)

imgs = [ cropped_img1, cropped_img2 ]
# # pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted( [(np.sum(i.size), i.size ) for i in imgs])[0][1]
imgs_comb = np.hstack( (np.asarray( i.resize(min_shape) ) for i in imgs ) )

# save that beautiful picture
imgs_comb = PIL.Image.fromarray( imgs_comb)
imgs_comb.save( 'Trifecta.png' ) 

[goncalopp]

I think the reason you're getting the wrong order without horizontalization is that your adaptiveThreshold is too conservative - you can still see the squares around the numbers, and ideally those should be gone before you pass the image to the OCR. I'd try and change C until those are gone.

In any case, I'm glad you got it to work! :) I'm closing this ticket for now, but feel free to comment if you need any further help