Invizo: Arabic Handwritten Document Optical Character Recognition Solution
Alhossien Waly, Bassant Tarek, Ali Feteha, Rewan Yehia, Gasser Amr,, Walid Gomaa, Ahmed Fares

TL;DR
Invizo presents an end-to-end Arabic OCR system combining CNN and Transformer models, achieving high accuracy on handwritten and printed texts, suitable for real-world applications.
Contribution
The paper introduces a novel Arabic OCR solution integrating CNN and Transformer architectures, addressing script variability and noise, with comprehensive detection and recognition capabilities.
Findings
81.66% precision in text detection
0.59% CER on printed text
7.91% CER on handwritten text
Abstract
Converting images of Arabic text into plain text is a widely researched topic in academia and industry. However, recognition of Arabic handwritten and printed text presents difficult challenges due to the complex nature of variations of the Arabic script. This work proposes an end-to-end solution for recognizing Arabic handwritten, printed, and Arabic numbers and presents the data in a structured manner. We reached 81.66% precision, 78.82% Recall, and 79.07% F-measure on a Text Detection task that powers the proposed solution. The proposed recognition model incorporates state-of-the-art CNN-based feature extraction, and Transformer-based sequence modeling to accommodate variations in handwriting styles, stroke thicknesses, alignments, and noise conditions. The evaluation of the model suggests its strong performances on both printed and handwritten texts, yielding 0.59% CER and & 1.72%…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Computer Science and Engineering · Vehicle License Plate Recognition
