DEEP Learning Based Handwritten Recognition using Checkerboard Pattern | ||||
| Journal of Computing and Communication | ||||
| Article 7, Volume 4, Issue 2, July 2025, Page 89-99 PDF (1.02 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2025.446644 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Sameh Zarif 1; Mahmoud Hammam2; Marian Wagdy3
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| 1Department of Information Technology, Faculty of Computers and Information, Menoufia University, Menoufia, Egypt | ||||
| 2MSc student at Banha University | ||||
| 3Department of Information Technology, Faculty of Computers and Information, Tanta University, Tanta, Egypt | ||||
| Abstract | ||||
| This research addresses Egypt's Vision 2030 by developing an AI system for automating optical character recognition for documents. This paper uses a deep learning to eliminate manual data entry and improve data accuracy, saving time and resources for organizations, which aligns with the Vision's goals of using AI for document processing and better decision-making. The paper uses mainly the checkerboard pattern to fix the de-warpping data on the bills, then the proposed method will be able to recognize the content of each bill separately and categorize the data inside those bills. The proposed method uses the mix of image correction for de-warpping the image and the usage of LSTMs to recognize the oriented text in the handwritten document. Also the checkerboard is aiding in the usage of the LSTM algorithm which advances its performance to be better than the other state of the art techniques for both the RIMES and IAM datasets. | ||||
| Keywords | ||||
| LSTM; Checkerboard pattern; Handwritten recognition; OCR; document processing | ||||
| References | ||||
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