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After the Islamic world expanded, many non-Arab Muslims found it difficult to read the Quran and distinguish between Arabic letters. Arab grammarians adjusted the text of the Quran to avoid distortion; therefore, dots were introduced.
The second half of the second A. The arrival of paper in the region contributed to the increase in material documented during that time. Paper was known in China five centuries before Islam. Arabs were introduced to paper in C. The number of writing scripts increased. In the 3rd A. H 9th C. The dominate scripts in the period of the 4 th —7th Islamic century 10th to 13th C.
After 8th A. There was also a special interest in Arabic calligraphy in that period. Scholars of Arabic history, scripts and texts consider division on the basis of Islamic centuries to be a better fit.
In the development of KERTAS dataset, special care has been taken to find and gather a significant number of manuscripts from each century.
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We have also attempted to properly verify the writing date of each manuscript in the database. This was not a simple task especially since there are quite a few manuscripts with ambiguous or incorrect writing dates. We checked the writing dates of these manuscripts from multiple sources before adding them to the database While collecting the database, we faced another very complicated issue regarding the writing date of certain manuscripts. In the later centuries, some manuscripts were copied by hand from earlier works.
In this case, what would the manuscript writing date be and who would be the author? Ideally, the date should be the writing date of the original manuscript and the author should be the author of the original manuscript. For the algorithms attempting to automatically identify the author or writing date based on the style of writing, the name of the copier and the date of copying should be selected.
In this dataset, we have solved this issue by recording the name of the copier and date of copying with the manuscript. This is done in order to provide a consistent view of the data for the author and writing date recognition algorithms.
KERTAS dataset not only contains a large number of images overbut they are quite nicely divided over the 14 Islamic centuries. The storage structure of the dataset is shown in Fig. Manuscript is the main directory with a subdirectory for each century. Within each century subdirectory, there are additional directories for each manuscript.
These individual manuscript directories contain images of multiple pages from these manuscripts. These online resources contain images from manuscripts written mainly in Latin.
The biggest drawback of these resources is that, images need to be downloaded one at a time and even then, not all images are useful for testing algorithms. Therefore, these images need to be sorted and selected once they are downloaded. This is a time-consuming and tedious process. There are a few datasets available that can be used for manuscript author identification; [ 7101920 ] are some of the major ones.
KERTAS dataset was designed particularly to assist in the effective training and testing of algorithms for document writer and age detection. That being said, the dataset is diverse and large enough to be equally useful in testing other algorithms such as document segmentation algorithms, text line and word extraction algorithms. The images selected to be the part of the dataset are also selected from a diverse set of documents ranging from manuscripts on mathematics, physics, Islamic history, metaphysics, etc.
This diverse nature of these manuscripts provides some unique and challenging images which can be used to test the limits of the algorithms being tested. Some images from manuscripts on mathematics contain drawings of figures and shapes Fig. These figures and tables are generally drawn with different color ink that are lighter than the text. In addition, the dataset also contains images of pages with comments on the margins written by different authors and different writing styles.
These images can be particularly challenging for writer identification algorithms and can help in designing robust writer identification algorithms. A new matrix A is defined as a concatenation of the n training images of all k centuries. Different image sizes are tested to select the optimal feature set size.
The rationale behind reducing image size is two folds: Firstly, reducing the image size reduces the dimensionality of the data thus producing a better underdetermined linear system which in term provided a better optimization solution. Secondly, the smaller variations such as noise and blemished are automatically removed thus providing an inherent robustness to the whole process. There is an issue with reducing the image size, i. This reduces the possibility of a correct match.
On the flip side, increasing the image size increases the dimensionality and thus reducing the chance of a better optimization solution. We also tested the algorithm with two different splits for the dataset.
This split enables us to train the algorithm with maximum variance of data. To evaluate the previous approach, we compare the results from sparse representation-based with some of state-of-the-art methods for writing style detection.
First, the dataset was preprocessed to be used with writing style-based features. Text area was segmented and binarized using Otsu threshold method [ 27 ]. Afterward, features were extracted by run length, edge hinge and edge direction methods.
Acknowledgements The authors gratefully acknowledge use of the services and facilities of the Qatar National Library. Compliance with ethical standards Conflict of interest The authors declare that they have no conflict of interest. Falagas, M. Bellier, C.Add Automatic Date in Excel
Metzger, B. Yeandle, L. Am Arch 43 3— Google Scholar. Rehbein, M. Aiolli, F. Essays Mem. Antonina Starita53—66 He, S. IEEE Trans. Image Process. Wahlberg, F.
ACM Google Scholar. Howe, N. Abulhab, S. At, E. Syrian Cultural Ministry, Damascus Gacek, A. Le Bourgeois, F. Springer, Berlin Google Scholar. Feuerverger, A. Sen, pp. Institute of Mathematical Statistics Google Scholar.
Fecker, D. Garain, U. Farrahi Moghaddam, R. Fernandez-Mota, D. International Conference on Pattern Recognition, pp. Wright, J. Pattern Anal. Djeddi, C. Pattern Recognit. Bulacu, M. Brink, A. Siddiqi, I. Turk, M. Otsu, N. Man Cybern. Personalised recommendations. Cite article How to cite? B Somaya Al-Maadeed. Northumbria University Newcastle, Newcastle upon T yne. These digital copies are. The publication or writing date of a historical manuscript.
It can help them. Knowing when the manuscript. Traditionallyhistorians and paleographers have used inva.
Automatic dating in word - Want to meet eligible single man who share your zest for life? Indeed, for those who've tried and failed to find the right man offline. PDF | The age of a historical manuscript can be an invaluable source of information for paleographers and historians. The process of automatic. Not to be too negative y'all, but courtship in the digital age is a bunch of awkward, incongruent nonsense. I hope I'm not alone in thinking that.
A few researchers have also. As men. Classifying ancient documents based on writing styles is. SPI uses tangent distance and sta. Afterward, SPI uses the models to measure similarity of the.
Moreover, He et al. Alternative research on dating. The descriptor is later sent to self-organizing. Similarly, W ahlberg et al. Howe et al. While there are quite a few online libraries with datasets. Howevermost researchers had to develop their own datasets.
A brief. The next section provides a brief history of Arabic. The design process. Section 5. Results and discussion. Then, conclusions are presented in. While Arabic scripts existed before Islam, Arab was an oral. Only a few inscriptions were found that. Figure 1 shows one of the pre-Islamic. The inscription was written with al Jazm, one. Islamic calendar Hijri started at C. Dates in. Islamic calendar are denoted A. Latin: Anno Hegirae. After the Islamic.
Arab grammarians adjusted the text of the Quran to avoid. The second half of the second A.
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Scholars of Arabic. Therefore, manuscripts. In the development of. W e have also attempted to properly verify the writing.
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For the algorithms attempt. Name of sources Number of manuscripts. University of Tubingen, Berlin 1. Princeton University Library 4. Y ale University 4. Islamic A warness Web site In this dataset, we have solved.
This is done in order to pro. T able 1 shows the sources of. T able 1 also shows that Qatar National Library is the main. The storage structure of the dataset is shown in Fig. Manuscript is the main directory with a subdirectory for. W ithin each century subdirectory, there are.
These individual. Figure 5 b presents the content of. Each manuscript. Figure 5 c. It is important to note that there are quite a few manuscript. The institute. These online resources contain images.
The biggest draw. Therefore, these images need to be sorted and. This is a time-consuming. There are a few datasets available that. T able 2 shows these datasets. The main issue with these datasets is that with the excep.
In addi. MPS dataset is the most. In fact, MPS was the tem.
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The aim was. Name Language Size. Syriac character [ 10 ] Syriac 60 K characters. Spanish book, Scale MPS [ 7 ]. Medieval Dutch charters. That being said, the dataset is. The images selected to. This diverse nature. Some images from manuscripts on mathemat.
cO Association for Computational Linguistics. Temporal Text Ranking and Automatic Dating of Texts. Vlad Niculae1, Marcos Zampieri2. automatic dating of a collection of about charters from medieval. Denmark, in particular how n-gram models based on different transcrip- tion levels of the. The age of a historical manuscript can be an invaluable source of information for paleographers and historians. The process of automatic.
In addition, the dataset also contains images. Figure 6 a, b shows. These images can be particularly. Deciding on the most suitable feature extraction method is. Age detection of historical manuscripts is a very. W e have to contend with the complex. The class boundaries. This means that for doc.
In addition, handwritten documents by different. It is interesting to note that a similar kind of interclass sim. Wright et al. In this paper, we are following the same. This sparse representation provides new insight into the. This theory of com. It uses.
In simplest terms, the algorithm works as follows. Given n i training samples of the i th centurythe matrix A i. Equation 4 represents an under. The similarity of handwriting style between manuscripts. These features are run-length feature as it was examined in. Run length is a multi-scale run feature that is obtained. Run-length feature is calculated after. Subsequently, probability. The approach is thoroughly explained in. Edge hinge is obtained by calculating normalized his.
While edge direction is. Both Edge hinge and Edge direction have been used in writer. The proposed approach presented above is ev aluated on. The exper. Every attempt. The remaining images are kept as part the. Different image sizes are created by scaling the image to. W e used the whole. Different image sizes are tested to select the optimal fea. The rationale behind reducing image size is two. Secondly, the smaller v ariations such as noise. There is an issue.
Table 3 Evaluation results for the sparse representation-based. Image size Accurac y with. Run length [ 22 ] Edge direction [ 23 ] Edge hinge [ 23 ] W e also tested the algorithm with. This split enables us to train the algorithm with max. The second split of the dataset uses. The results. The results show that as we increase the image size, ini. The highest accuracy is obtained at the image size of. Howeverif we continue to increase the image size. T o evaluate the previous approach, we compare the results.
First, the dataset was. Te xt area was segmented and binarized using Otsu thresh. Afterward, features were extracted by run.
W e evaluated. T able 4 presents the results of using writing. In addition, we pre.
The frequency of occurrence of words in natural languages exhibits a periodic and a non-periodic component when analysed as a time series.
The algorithm also provides a baseline accuracy. The dataset. Acknowledgements The authors gratefully acknowledge use of the ser.