# Invizo: Arabic Handwritten Document Optical Character Recognition Solution

arXiv: https://arxiv.org/abs/2502.05277 · HTML: https://mlsanity.com/paper/2502.05277/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Shape Robust Text Detection with Progressive Scale Expansion Network](https://mlsanity.com/paper/1903.12473/) (2019)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [TextBoxes: A Fast Text Detector with a Single Deep Neural Network](https://mlsanity.com/paper/1611.06779/) (2016)
- [TextBoxes++: A Single-Shot Oriented Scene Text Detector](https://mlsanity.com/paper/1801.02765/) (2018)
- [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://mlsanity.com/paper/2202.10304/) (2022)
- [Rotation-Sensitive Regression for Oriented Scene Text Detection](https://mlsanity.com/paper/1803.05265/) (2018)
- [Detecting Oriented Text in Natural Images by Linking Segments](https://mlsanity.com/paper/1703.06520/) (2017)
- [Single Shot Text Detector with Regional Attention](https://mlsanity.com/paper/1709.00138/) (2017)
- [Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping](https://mlsanity.com/paper/1807.03547/) (2018)
- [Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition](https://mlsanity.com/paper/2407.13559/) (2024)
- [DeRPN: Taking a further step toward more general object detection](https://mlsanity.com/paper/1811.06700/) (2018)
- [Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection](https://mlsanity.com/paper/2412.01601/) (2024)

## Abstract (arXiv)

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.