Invizo: Arabic Handwritten Document Optical Character Recognition Solution

2025-02-07

AI Summary

This study addresses the end-to-end OCR challenge for Arabic handwritten and printed text—including numerals—by proposing a robust document parsing framework integrating CNNs and Transformers. Methodologically, it employs multi-scale text detection and adaptive binarization for improved localization robustness; pioneers the fusion of CNN-based feature extraction with Transformer-based sequential modeling to capture handwriting variability; and introduces a joint CTC-Attention decoding mechanism to uniformly handle mixed handwritten, printed, and numeric text. Experiments yield a text detection F-measure of 79.07%, a character error rate (CER) of 0.59% on printed text and 7.91% on handwritten text. The system demonstrates strong generalization on real-world receipts and document images, meeting industrial deployment requirements. Its core contribution is the first end-to-end Arabic OCR system explicitly designed to accommodate the high diversity of handwritten forms while ensuring structured, reliable output.

Technology Category

• Natural Language Processing: Learning & Optimization for NLP
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Algorithmic accountability and transparency on the web

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% WER on printed text, and 7.91% CER and 31.41% WER on handwritten text. The overall proposed solution has proven to be relied on in real-life OCR tasks. Equipped with both detection and recognition models as well as other Feature Extraction and Matching helping algorithms. With the general purpose implementation, making the solution valid for any given document or receipt that is Arabic handwritten or printed. Thus, it is practical and useful for any given context.

Problem

• Arabic handwritten OCR solution
• Complex script variations handling
• High accuracy text recognition

Innovation

• CNN-based feature extraction
• Transformer-based sequence modeling
• End-to-end Arabic OCR solution