Handwritten Arabic Bills Reader and Recognizer | ||||
| Journal of Computing and Communication | ||||
| Article 4, Volume 3, Issue 1, January 2024, Page 44-54 PDF (860.51 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2024.339920 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Sara Sweidan  1; Mahmoud Hammam2
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| 1Artificial intelligence Department, faculty of computers& Artificial Intelligence, Benha University | ||||
| 2Faculty of Computers and Artificial Intelligence, Artificial Intelligence Department, Benha University, Benha 13512, Egypt Faculty of Artificial Intelligence, Egyptian Russian University, Cairo 11829, Egypt | ||||
| Abstract | ||||
| In pursuit of Egypt's Vision 2030, which emphasizes the pivotal role of governance within state institutions and society, artificial intelligence (AI) stands as a transformative force. A central tenet of this vision involves harnessing AI technologies to accelerate the digitization of documents and their seamless integration into a unified system. This fosters more informed decision-making processes and revolutionizes processing and utilizing information. Our current research project aligns with this broader goal by deep learning capabilities to support organizations involved in enterprise applications. By incorporating AI-driven solutions, we aim to empower these organizations to manage their operations and optimize resource allocation efficiently. The proposed model eliminates manual input of handwritten invoices into ERP applications, resulting in substantive cost savings. Furthermore, the proposed model integrates an entity classification system enhanced by LSTM, significantly improving invoice data's clarity and accuracy. This streamlined approach saves valuable time and enhances the overall effectiveness of resource allocation and decision-making processes. In essence, by integrating AI into document management and enterprise operations, we are not only contributing to the realization of Egypt's vision but also spearheading a technological transformation that has far-reaching implications for governance, efficiency, and progress in the digital age. | ||||
| Keywords | ||||
| Handwritten Bills; Deep learning; Named Entity Recognition; LSTM | ||||
| References | ||||
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