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

arXiv: https://arxiv.org/abs/2412.01601 · HTML: https://mlsanity.com/paper/2412.01601/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://mlsanity.com/paper/2202.10304/) (2022)

## Built on by (most cited first, 1 of 1)

- [Invizo: Arabic Handwritten Document Optical Character Recognition Solution](https://mlsanity.com/paper/2502.05277/) (2025)

## Abstract (arXiv)

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.