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

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Alhossien Waly, Bassant Tarek, Ali Feteha, Rewan Yehia, Gasser Amr, Ahmed Fares

Computer Vision and Pattern Recognition · Submitted 2 Dec 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim These results demonstrate the system's strong performance in handling Arabic scripts, establishing a new benchmark for AHTR systems.

how it is stated · asserted 1.00 read the record →

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arXiv ID2412.01601 (version 1)
Pith ID7UIV2HU7, Pith's permanent record of this paper and its review
CitationAhmed Fares, Alhossien Waly, Ali Feteha, Bassant Tarek, Gasser Amr, Rewan Yehia. "Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection." arXiv:2412.01601 (2024). https://pith.science/paper/7UIV2HU7
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,993 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.32 · human participants 0.04 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.17 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The problem of converting images of text into plain text is a widely researched topic in both academia and industry. background 1.00 0.03 0.02 asserted 1.00
s1 Arabic handwritten Text Recognation (AHTR) poses additional challenges due to diverse handwriting styles and limited labeled data. background 0.92 0.05 0.02 asserted 1.00
s2 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. method 0.76 0.87 0.05 asserted 0.99
s3 Following segmentation, a CNN-BiLSTM-CTC architecture is applied to recognize characters. method 1.00 0.82 0.03 asserted 0.99
s4 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. result 1.00 0.97 0.23 asserted 1.00
s5 These results demonstrate the system's strong performance in handling Arabic scripts, establishing a new benchmark for AHTR systems. interpretation 1.00 0.64 0.88 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.94.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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