Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection

• URL: http://arxiv.org/abs/2412.01601v1
• Date: Mon, 02 Dec 2024 15:21:09 GMT
• Title: Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection
• Authors: Alhossien Waly, Bassant Tarek, Ali Feteha, Rewan Yehia, Gasser Amr, Ahmed Fares,
• Abstract summary: We present a complete OCR pipeline that starts with line segmentation and Adaptive Scale Fusion techniques to ensure accurate detection of text lines. Our system, trained on the Arabic Multi-Fonts dataset, achieves a Character Recognition Rate (CRR) of 99.20% and a Word Recognition Rate (WRR) of 93.75% on single-word samples containing 7 to 10 characters.
• Score: 1.1655046053160683
• License:
• Abstract: The problem of converting images of text into plain text is a widely researched topic in both academia and industry. Arabic handwritten Text Recognation (AHTR) poses additional challenges due to diverse handwriting styles and limited labeled data. In this paper we present a complete OCR pipeline that starts with line segmentation using Differentiable Binarization and Adaptive Scale Fusion techniques to ensure accurate detection of text lines. Following segmentation, a CNN-BiLSTM-CTC architecture is applied to recognize characters. Our system, trained on the Arabic Multi-Fonts Dataset (AMFDS), achieves a Character Recognition Rate (CRR) of 99.20% and a Word Recognition Rate (WRR) of 93.75% on single-word samples containing 7 to 10 characters, along with a CRR of 83.76% for sentences. These results demonstrate the system's strong performance in handling Arabic scripts, establishing a new benchmark for AHTR systems.

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