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        "author": "VasistaReddy",
        "author_email": "vasista.1245@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: Freeware",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Software Development :: Version Control :: Git"
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        "description": "# Project Description\n\n\t- RUN LENGTH SMOOTHING ALGORITHM(RLSA) is a method mainly used for block segmentation and text discrimination.\n\t- It mainly uses in Document Image Processing to extract out the ROI(region of interest) like block-of-text/title/content with applied heuristics.\n\n# Install requirements\n\n\t- pip install -r requirements.txt\n\n# Install\n\n\t- pip install pythonRLSA\n\n# Input & Output\n\n[Output of 3 cases with value \"10\" can be seen here](https://github.com/Vasistareddy/pythonRLSA/tree/master/pythonRLSA/test_images/image1.png)\n\nMore results can be seen [here](https://github.com/Vasistareddy/pythonRLSA/tree/master/pythonRLSA/test_images)\n\n# How it works\n\n\t- '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\".\n\t- The \"value\" varies among the different images.\n\n# Sample Test Case\n\n\t- value = 3\n\t- input - [0, 0, 255, 255, 255, 0, 0, 255, 0, 0, 255, 0, 255]\n\t- output - [0, 0, 255, 255, 255, 0, 0, 0, 0, 0, 0, 0, 255]\n\n# To test\n\n\t- python pythonRLSA/test_rlsa_unittest.py -v\n\n# Unittest Results\n\n\t- test_bool (__main__.TestRLSA) ... ok\n\t- test_image (__main__.TestRLSA) ... Image must be an numpy ndarray and must be in binary ... ok\n\t- test_rlsa_hori (__main__.TestRLSA) ... ok\n\t- test_rlsa_hori_vert (__main__.TestRLSA) ... ok\n\t- test_rlsa_vert (__main__.TestRLSA) ... ok\n\t- test_value (__main__.TestRLSA) ... ok\n\n----------------------------------------------------------------------\nRan 6 tests in 0.003s\n\nOK\n\n# Prerequisites\n\n\t- python3.5+\n\t- Image must be a binary ndarray(255's/1's/0's)\n\t- Must pass a predefined limit, a certain integer \"value\"\n\n# Method\n\n\t- rlsa\n\n# Parameters\n\n\t- image - numpy.ndarray(required)\n\t- horizantal - boolean(required)\n\t- vertial - boolean(required)\n\t- value - any positive integer(int)(required)\n\n# IPython code to convert Image to Binary and RLSA usage\n\n\t- # convert the image to binary\n\t- import cv2\n\t- image = cv2.imread('test_images/image.jpg')\n\t- gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\t- (thresh, image_binary) = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n\t- # function call\n\t- from pythonRLSA import rlsa\n\t- image_rlsa_horizontal = rlsa.rlsa(image_binary, True, False, 10)\n\n# Bugs/Errors\n\nPlease ensure that you have updated pip to the latest version before installing pythonRLSA.\n\nIf you find any bugs/errors in the usage of above code, please raise an issue through [Github](https://github.com/Vasistareddy/pythonRLSA) or send an email to vasista.1245@gmail.com with a clear example that can reproduce the issue.\n\n\n\n\n\n\n\n",
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