Package: pythonRLSA
RUN LENGTH SMOOTHING ALGORITHM(RLSA) is a method mainly used for block segmentation and text discrimination.It is mainly used in Document Image Processing to extract out the ROI(region of interest) like block-of-text/title/content with applied heuristics.Read the application of RLSA here ->Extract Title from the Image documents in pythonLatest Updates- now rlsa function accepts single value/tuple with pair of values. (before we need to call the function twice)
- a single value be assign to both operations.
- a tuple pair of values be assign to horizontal and vertical operations respectively
- package has a method rlsa_fast that can perform Run Length Smoothing in real time for operations where time is a constraintInstall- pip install pythonRLSAInstall from Source- python setup.py installInstall requirements- pip install -r requirements.txtFunction Calls Snippet - Various Combinationsfrom pythonRLSA import rlsa

rlsa.rlsa(image_binary, True, True, (10,5)) # passing different values for H and V operations
rlsa.rlsa(image_binary, True, True, [10,5]) # passing different values for H and V operations
rlsa.rlsa(image_binary, True, True, (10)) # passing same value but in tuple
rlsa.rlsa(image_binary, True, True, [10]) # passing same value but in list
rlsa.rlsa(image_binary, True, True, 10) # passing same value as int
rlsa.rlsa(image_binary, True, False, 10.0) # passing same value as float
rlsa.rlsa(image_binary, False, True, 10) # passing same value as int for V operation only

* H - Horizontal
* V - Vertical

- At the end of the readme, ipython snippet is attachedfrompythonRLSA.rlsa_fastimportrlsa_fastH_V=rlsa_fast(image_binary,True,True,10)# performing Horizontal and Vertical operationsH=rlsa_fast(image_binary,True,False,10)# performing Horizontal operationV=rlsa_fast(image_binary,False,True,10)# performing Vertical operationInput & OutputOutput of 3 cases with value "10" can be seen in the below imageMore sample images can be seen hereHow it works- '255'(white pixel) wil be converted to '0'(black pixel) in a image, if the number of adjacent 255's are less than the predefined limit "value".
- The "value" varies among the different images.Sample Test Case- value = 3
- input - [0, 0, 255, 255, 255, 0, 0, 255, 0, 0, 255, 0, 255]
- output - [0, 0, 255, 255, 255, 0, 0, 0, 0, 0, 0, 0, 255]To test- python pythonRLSA/test_rlsa_unittest.py -v
- python rlsa_fast/test_rlsafast_unittest.py -vUnittest Results$ test_bool (__main__.TestRLSA) ... ok
$ test_image (__main__.TestRLSA) ... Image must be an numpy ndarray and must be in binary ... ok
$ test_rlsa_hori (__main__.TestRLSA) ... ok
$ test_rlsa_hori_vert (__main__.TestRLSA) ... ok
$ test_rlsa_vert (__main__.TestRLSA) ... ok
$ test_value (__main__.TestRLSA) ... okRan 6 tests in 0.003sOKPrerequisites- python3.5+
- Image must be a binary ndarray(255's/1's/0's)
- Must pass a predefined limit, a certain integer "value"Method- rlsa
- rlsa_fastParameters- image - numpy.ndarray(required)
- horizantal - boolean(required)
- vertial - boolean(required)
- value - any positive integer(int)/list/tuple(required)IPython snippet to convert Image to Binary and RLSA usage# convert the image to binary
import cv2
image = cv2.imread('test_images/image.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
(thresh, image_binary) = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# function call
from pythonRLSA import rlsa
image_rlsa_horizontal = rlsa.rlsa(image_binary, True, False, 10)
image_rlsa_horizontal_vertical = rlsa.rlsa(image_binary, True, True, [10,5])Bugs/ErrorsPlease ensure that you have updated pip to the latest version before installing pythonRLSA.If you find any bugs/errors in the usage of above code, please raise an issue throughGithubor send an email tovasista.1245@gmail.comwith a clear example that can reproduce the issue.