Optical Character Recognition of Image of Cuneiform Characters (Ugarit Alphabet)

Authors

  • Hasan Al-Ahmad
  • Eyad khaddor

Keywords:

Optical Character Recognition, Image Processing, Digital Image Segmentation, Cuneiform Language, Stone Manuscripts, Classification

Abstract

The research aims to provide a new contribution to the process of developing an effective algorithm for the reading Ugaritic manuscripts based on the extraction of shape and geometric features of the character. At the beginning, the text image is pre-processed to achieve the least possible noise and the highest resolution in the image to adjust the gray levels by adaptive histogram equalization. Second, the image is transformed into binary image using the dynamic Utso algorithm. The text is then segmented into its constituent lines and then the characters of each line, depending on the morphological processes, followed by the stage of extracting the geometric and morphological features of each of the segmented characters and then identifying the most important features. Before the recognition process, there is still a very important step which is the training stage forms the basis of the subsequent identification process, in which a database is produced for different characters forms in different forms and situations.

Database are stored for use the test phase.

In the last stage, the text that is stored as a test image is recognized. It is entered into the computer program. The image is processed with the same image processing steps used before. The step is the segmentation of lines and then basic characters of the image.  Features of each character are then extracted and compared with the features of all the models in the pre-stored database, Their similarities are tested based on several suggested classifiers: the Neural Network Categorizer, the SVM classifier, and the Minimal Distance Classifier (MDC) to select the character with the greatest similarity value.

After Segmentation process, we obtained 270 cuneiform characters and the correct segmentation rate was 97.4%. The system was tested on nine different scenarios and the recognition rate was 93.33% with an average time of 0.186 seconds.

Downloads

Published

2020-10-01

How to Cite

1.
الأحمد ح, خضور ا. Optical Character Recognition of Image of Cuneiform Characters (Ugarit Alphabet). Tuj-eng [Internet]. 2020Oct.1 [cited 2024Apr.24];42(4). Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/9931

Issue

Vol. 42 No. 4 (2020): Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series

Section

Articles

License

Copyright (c) 2020 ttps://creativecommons.org/licenses/by-nc-sa/4.0/

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The authors retain the copyright and grant the right to publish in the magazine for the first time with the transfer of the commercial right to Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series

 Under a CC BY- NC-SA 04 license that allows others to share the work with of the work's authorship and initial publication in this journal. Authors can use a copy of their articles in their scientific activity, and on their scientific websites, provided that the place of publication is indicted in  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series   .  The Readers have the right to send, print and subscribe to the initial version of the article, and the title of  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series     Publisher

journal uses a CC BY-NC-SA license which mean

You are free to:

  • Adapt — remix, transform, and build upon the material
  •  
  • The licensor cannot revoke these freedoms as long as you follow the license terms.

 

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.