The Incremental Artificial Immune System for Arabic Handwritten Recognition

The Incremental Artificial Immune System for Arabic Handwritten Recognition

Khelil Hiba, Benyettou Abdelkader, Afef Kacem
Copyright: © 2019 |Pages: 19
DOI: 10.4018/JITR.2019100105
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The historical document is a treasure. The frequent use of these documents requires having a numeric copy. The use of these numeric documents requires developing techniques to facilitate their use. The search by content, the word spotting, and handwriting recognition became important points of research in document analysis. For this purpose, in this article is covered the recognition of the Arabic manuscript names extracted from the register of names of the Tunisian national archive. In the study, the authors have used several techniques for extracting knowledge, coding, and name recognition. The authors have also optimized the clonclas algorithm using the incremental principle from the i2gng algorithm. The results encourage continuing exploration.
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2. Register Of Names From The Tunisian National Archive

The data base used in this article is extracted from the register of names from the Tunisian national archive. The used register is composed of 32 pages where each one contains environs a hundred names. The Figure 1 shows a page of this register.

Figure 1.

Example of one page extracted from register of names


Once the page is scanned, segmentation into lines and words was applied. The extracted words constitute the database to be used for learning and classification in our system. The authors identified 234 different words with a different appearance frequency. Here, some examples of the database (see Figure 2).

Figure 2.

Some examples of the data base of names: (a) Mohammed, (b) Zouaoui, (c) Ahmed, (d) Feth, (e) Derdour, (f) Dhouadi


3. Methodology Of Work

In this article, the authors use approaches-oriented image processing and off-line handwriting recognition. First, authors prepare the input data using two methods, the first method use the NSHP-HMM (Choisy, 2006) and the second use the structural features extraction (Kacem, Aouiti, & Belaîd, 2012; Khelil, Kacem, Belaîd, & Benyettou, 2012). The authors have chosen the NSHP-HMM method because it is an elastic model used to normalize the input images to a standard size. It searches the important features and absorbs the distortions. The structural features extraction method is a classical method which gives structural properties of the image words (Choisy, 2006).

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