Identification of Fraktur and Latin Scripts in German Historical Documents Using Image Texture Analysis

Figures & data

Figure 1. The flow of the multistage script discrimination method.

Figure 2. Script-type definition: (a) Latin script, (b) Fraktur script.

Table 1. Coding of German alphabets.

Latin	Coding	Fraktur	Coding	Latin	Coding	Latin	Coding
A	2	A	2	a	1	a	1
B	2	B	2	b	2	b	2
C	2	C	2	c	1	c	1
D	2	D	2	d	2	d	2
E	2	E	2	e	1	e	1
F	2	F	4	f	2	f	4
G	2	G	2	g	3	g	3
H	2	H	4	h	2	h	4
I	2	I	2	i	2	i	2
J	2	J	4	j	4	j	4
K	2	K	2	k	2	k	2
L	2	L	2	l	2	l	2
M	2	M	2	m	1	m	1
N	2	N	2	n	1	n	2
O	2	O	2	o	1	o	2
P	2	P	4	p	3	p	3
Q	2	Q	2	q	3	q	3
R	2	R	2	r	1	r	1
S	2	S	2	s	1	s	1
T	2	T	2	t	2	t	1
U	2	U	2	u	1	u	1
V	2	V	2	v	1	v	1
W	2	W	2	w	1	w	1
X	2	X	2	x	1	x	3
Y	2	Y	4	y	3	y	3
Z	2	Z	4	z	1	z	3

Table 2. Coding of German diacritics.

Latin	Coding	Fraktur	Coding	Latin	Coding	Latin	Coding
Ä	2	Ä	2	ä	2	ä	2
Ö	2	Ö	2	ö	2	ö	2
Ü	2	Ü	2	ü	2	ü	2
-		-		ß	2	ß	4

Figure 3. German text: (a) Latin script, (b) Latin text coding, (c) Latin coded text, (d) Fraktur script, (e) Fraktur text coding, (f) Fraktur coded text.

Figure 4. WOI for the calculation of texture features.

Figure 5. Statistical analysis of the text: (a) normalized GLCM for Latin script, (b) normalized GLCM for Fraktur script.

Table 3. Comparison of the first-order texture descriptors between Latin and Fraktur scripts.

First-order descriptors	Latin script	Fraktur script	Latin/Fraktur
mean μx	1.4514	1.6778	0.8650
mean μy	1.4512	1.6774	0.8651
standard deviation σx	0.5615	0.8634	0.6503
standard deviation σy	0.5615	0.8631	0.6505

Table 4. Comparison of the second-order texture descriptors between Latin and Fraktur scripts.

Second-order descriptors	Latin script	Fraktur script	Latin/Fraktur
energy	0.2604	0.1596	1.6315
entropy	−1.4985	−2.0675	0.7247
maxi	0.3768	0.2754	1.3681
dissimilarity	1.0000	0.9855	1.0147
contrast	2.0725	2.0725	1
invdmoment	0.6072	0.6159	0.9858
homogeneity	0.6715	0.6504	1.0324
correlation	−0.0149	−0.1403	0.1062

Table 5. Comparison of the first-order texture descriptors between Latin and Fraktur scripts extracted from training part of the database.

First order descriptors		Latin script	Fraktur script
mean μx	Minimum	1.4347	1.5934
	Maximum	1.6062	1.7099
mean μy	Minimum	1.4337	1.5927
	Maximum	1.6065	1.7095
standard deviation σx	Minimum	0.5491	0.7142
	Maximum	0.7616	0.8889
standard deviation σy	Minimum	0.5490	0.7142
	Maximum	0.7615	0.8891

Figure 6. First order texture descriptors obtained from Latin and Fraktur scripts extracted from training part of database: (a) mean μ_x, (b) mean μ_y, (c) standard deviation σ_x, and (d) standard deviation σ_y.

Table 6. The second--order texture descriptors obtained from the training part of the database written in Latin and Fraktur scripts.

Second-order descriptors		Latin script	Fraktur script
energy	Minimum	0.2360	0.1519
	Maximum	0.2475	0.1750
entropy	Minimum	−1.5722	−2.1075
	Maximum	−1.4949	−1.9517
maxi	Minimum	0.2709	0.2190
	Maximum	0.2871	0.2404
dissimilarity	Minimum	0.5965	0.9210
	Maximum	1.1987	1.0369
contrast	Minimum	0.6609	1.7259
	Maximum	2.4551	2.0291
invdmoment	Minimum	0.5263	0.5808
	Maximum	0.7082	0.6200
homogeneity	Minimum	0.6044	0.6106
	Maximum	0.7112	0.6459
correlation	Minimum	−0.1810	−0.1863
	Maximum	−0.1299	−0.1186

Figure 7. Second-order texture descriptors obtained from Latin and Fraktur scripts extracted from training part of the database: (a) energy, (b) entropy, (c) maximum, (d) dissimilarity, (e) contrast, (f) inverse difference moment, (g) homogeneity, and (h) correlation.

Table 7. Comparison of the first-order texture descriptors between Latin and Fraktur scripts extracted from the test part of the database.

First-order descriptors		Latin script	Fraktur script
mean μx	Minimum	1.4379	1.6439
	Maximum	1.4755	1.7115
mean μy	Minimum	1.4374	1.6435
	Maximum	1.4750	1.7115
standard deviation σx	Minimum	0.5523	0.8488
	Maximum	0.5833	0.9002
standard deviation σy	Minimum	0.5522	0.8489
	Maximum	0.5833	0.9002

Table 8. The second-order texture descriptors obtained from the test part of the database written in Latin and Fraktur scripts.

Second-order descriptors		Latin script	Fraktur script
energy	Minimum	0.2312	0.1554
	Maximum	0.2443	0.1726
entropy	Minimum	−1.6250	−2.0823
	Maximum	−1.5518	−2.0097
maxi	Minimum	0.2767	0.2099
	Maximum	0.3380	0.2527
dissimilarity	Minimum	0.5974	0.8947
	Maximum	0.6242	0.9690
contrast	Minimum	0.6907	1.6523
	Maximum	0.7431	1.8997
invdmoment	Minimum	0.6969	0.6079
	Maximum	0.7108	0.6289
homogenicity	Minimum	0.7023	0.6361
	Maximum	0.7166	0.6549
correlation	Minimum	−0.1610	−0.2057
	Maximum	−0.0921	−0.1363

Table 9. Comparison of GA-ICDA on the training set (102 samples) with two other unsupervised classifiers (k-means and SOM).

Measure	nc*	Precision		Recall		F-Measure		NMI **
Classifier		Fraktur	Latin	Fraktur	Latin	Fraktur	Latin
K-Means	2	1,0000 (0,0000)	0,9808 (0,0000)	0,9804 (0,0000)	1,0000 (0,0000)	0,9901 (0,0000)	0,9903 (0,0000)	0,9302 (0,0000)
SOM	4	1,0000 (0,0000)	1,0000 (0,0000)	0,5380 (0,0581)	0,9678 (0,1102)	0,6980 (0,0443)	0,9799 (0,0690)	0,7391 (0,0153)
GA-ICDA	2	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)

*nc represents number of classes

**(Strehl and Ghosh 2003)

Table 10. Comparison of GA-ICDA on the test set (18 samples) with two other supervised classifiers.

Measure	nc*	Precision		Recall		F-Measure		NMI**
Classifier		Fraktur	Latin	Fraktur	Latin	Fraktur	Latin
Naïve-Bayes	2	0,5000 (0,0000)	0,5000 (0,0000)	0,8000 (0,0000)	1,0000 (0,0000)	0,6154 (0,0000)	0,6667 (0,0000)	0,1368 (0,0000)
SVM	1	0,5556 (0,0000)	0,4444 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)	0,7143 (0,0000)	0,6154 (0,0000)	0,0000 (0,0000)
GA-ICDA	2	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)	1,0000 (0,0000)

*nc represents number of classes

**(Strehl and Ghosh 2003)

Strehl, A., and J. Ghosh. 2003. Cluster ensembles a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3 (2003):583–617.

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