Prediction of case types from non-searchable pdf documents in arabic: Comparison of machine learning and deep learning with image processing
The study conducted focuses on predicting the different types of judicial cases presented to Moroccan administrative courts by using court decisions in the form of non-searchable PDF documents in the Arabic language. To achieve this, we utilized image processing, text cleaning techniques, and machin...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00110.pdf |
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| author | El Arrasse Mouad Khourdifi Youness Mounir Soufyane El Alami Alae |
| author_facet | El Arrasse Mouad Khourdifi Youness Mounir Soufyane El Alami Alae |
| author_sort | El Arrasse Mouad |
| collection | DOAJ |
| description | The study conducted focuses on predicting the different types of judicial cases presented to Moroccan administrative courts by using court decisions in the form of non-searchable PDF documents in the Arabic language. To achieve this, we utilized image processing, text cleaning techniques, and machine learning algorithms.We carried out a comparative study using both machine learning and deep learning techniques. The experiment was conducted in two phases: first on 697 court decisions, and then on 14,207 decisions from the Administrative Court of Appeal in Marrakech. Despite the challenges associated with the Arabic language, our methods were able to efficiently extract text, leading to accurate predictions. For the experiment on 697 decisions, machine learning achieved an accuracy rate of 91%, while deep learning reached 100%. For the experiment on 14,207 decisions, machine learning obtained an accuracy of 97%, and deep learning achieved 96%.As a result, this study contributes to the existing literature on the digitization and processing of unstructured documents in the Arabic language, as well as on the prediction of judicial case types through the use of machine learning and deep learning algorithms. |
| format | Article |
| id | doaj-art-796f8efc0fa94866928d6c5b7325bec9 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-796f8efc0fa94866928d6c5b7325bec92025-02-05T10:46:26ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010011010.1051/e3sconf/202560100110e3sconf_icegc2024_00110Prediction of case types from non-searchable pdf documents in arabic: Comparison of machine learning and deep learning with image processingEl Arrasse Mouad0Khourdifi Youness1Mounir Soufyane2El Alami Alae3National School of Applied Sciences of Khouribga (Laboratory of Engineering Science and Technology)University Sultan Moulay Slimane, Polydisciplinary Faculty of Khouribga (Laboratory of Materials Science, Mathematics and Environment)National School of Applied Sciences of Khouribga (Laboratory of Engineering Science and Technology)Higher School of Technology Meknès (Laboratory of Computer Engineering and Intelligent Electrical Systems)The study conducted focuses on predicting the different types of judicial cases presented to Moroccan administrative courts by using court decisions in the form of non-searchable PDF documents in the Arabic language. To achieve this, we utilized image processing, text cleaning techniques, and machine learning algorithms.We carried out a comparative study using both machine learning and deep learning techniques. The experiment was conducted in two phases: first on 697 court decisions, and then on 14,207 decisions from the Administrative Court of Appeal in Marrakech. Despite the challenges associated with the Arabic language, our methods were able to efficiently extract text, leading to accurate predictions. For the experiment on 697 decisions, machine learning achieved an accuracy rate of 91%, while deep learning reached 100%. For the experiment on 14,207 decisions, machine learning obtained an accuracy of 97%, and deep learning achieved 96%.As a result, this study contributes to the existing literature on the digitization and processing of unstructured documents in the Arabic language, as well as on the prediction of judicial case types through the use of machine learning and deep learning algorithms.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00110.pdfmachine learningdeep learningjudicial case predictionnonsearchable pdfsimage processingtext extraction |
| spellingShingle | El Arrasse Mouad Khourdifi Youness Mounir Soufyane El Alami Alae Prediction of case types from non-searchable pdf documents in arabic: Comparison of machine learning and deep learning with image processing E3S Web of Conferences machine learning deep learning judicial case prediction nonsearchable pdfs image processing text extraction |
| title | Prediction of case types from non-searchable pdf documents in arabic: Comparison of machine learning and deep learning with image processing |
| title_full | Prediction of case types from non-searchable pdf documents in arabic: Comparison of machine learning and deep learning with image processing |
| title_fullStr | Prediction of case types from non-searchable pdf documents in arabic: Comparison of machine learning and deep learning with image processing |
| title_full_unstemmed | Prediction of case types from non-searchable pdf documents in arabic: Comparison of machine learning and deep learning with image processing |
| title_short | Prediction of case types from non-searchable pdf documents in arabic: Comparison of machine learning and deep learning with image processing |
| title_sort | prediction of case types from non searchable pdf documents in arabic comparison of machine learning and deep learning with image processing |
| topic | machine learning deep learning judicial case prediction nonsearchable pdfs image processing text extraction |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00110.pdf |
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