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@pebbie
Last active Aug 14, 2019
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Implementation of document binarization algorithm by (Bolan Su et al, 2010)
"""
author: Peb Ruswono Aryan
Binarization Algorithm by Su et al.
@inproceedings{Su:2010:BHD:1815330.1815351,
author = {Su, Bolan and Lu, Shijian and Tan, Chew Lim},
title = {Binarization of Historical Document Images Using the Local Maximum and Minimum},
booktitle = {Proceedings of the 9th IAPR International Workshop on Document Analysis Systems},
series = {DAS '10},
year = {2010},
isbn = {978-1-60558-773-8},
location = {Boston, Massachusetts, USA},
pages = {159--166},
numpages = {8},
url = {http://doi.acm.org/10.1145/1815330.1815351},
doi = {10.1145/1815330.1815351},
acmid = {1815351},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {document image analysis, document image binarization, image contrast, image pixel classification},
}
"""
import sys
import numpy as np
import cv2
import os.path as path
nfns = [
lambda x: np.roll(x, -1, axis=0),
lambda x: np.roll(np.roll(x, 1, axis=1), -1, axis=0),
lambda x: np.roll(x, 1, axis=1),
lambda x: np.roll(np.roll(x, 1, axis=1), 1, axis=0),
lambda x: np.roll(x, 1, axis=0),
lambda x: np.roll(np.roll(x, -1, axis=1), 1, axis=0),
lambda x: np.roll(x, -1, axis=1),
lambda x: np.roll(np.roll(x, -1, axis=1), -1, axis=0)
]
def localminmax(img, fns):
mi = img.astype(np.float64)
ma = img.astype(np.float64)
for i in range(len(fns)):
rolled = fns[i](img)
mi = np.minimum(mi, rolled)
ma = np.maximum(ma, rolled)
result = (ma-mi)/(mi+ma+1e-16)
return result
def numnb(bi, fns):
nb = bi.astype(np.float64)
i = np.zeros(bi.shape, nb.dtype)
i[bi==bi.max()] = 1
i[bi==bi.min()] = 0
for fn in fns:
nb += fn(i)
return nb
def rescale(r,maxvalue=255):
mi = r.min()
return maxvalue*(r-mi)/(r.max()-mi)
if __name__=="__main__":
gfn = nfns
N_MIN = 4
if len(sys.argv)<2:
print sys.argv[0],"input-image output-image"
sys.exit(1)
try:
if len(sys.argv)>3: N_MIN = int(sys.argv[3])
except:
pass
to_screen = len(sys.argv)<3
fn = sys.argv[1]
img = cv2.imread(fn)
g = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
I = g.astype(np.float64)#/255.
if to_screen:cv2.imshow("I", g)
cimg = localminmax(I, gfn)
_, ocimg = cv2.threshold(rescale(cimg).astype(g.dtype), 0, 1, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
E = ocimg.astype(np.float64)
if to_screen:cv2.imshow("contrast", rescale(cimg,1.))
if to_screen:cv2.imshow("High contrast", E)
N_e = numnb(ocimg, gfn)
nbmask = N_e>0
E_mean = np.zeros(I.shape, dtype=np.float64)
for fn in gfn:
E_mean += fn(I)*fn(E)
E_mean[nbmask] /= N_e[nbmask]
E_var = np.zeros(I.shape, dtype=np.float64)
for fn in gfn:
tmp = (fn(I)-E_mean)*fn(E)
E_var += tmp*tmp
E_var[nbmask] /= N_e[nbmask]
E_std = np.sqrt(E_var)*.5
R = np.ones(I.shape)*255
R[(I<=E_mean+E_std)&(N_e>=N_MIN)] = 0
if to_screen:
cv2.imshow("Result", R)
cv2.waitKey(0)
else:
cv2.imwrite(sys.argv[2], R)
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