Invizo: Arabic Handwritten Document Optical Character Recognition Solution

TL;DR

Invizo presents an end-to-end Arabic OCR system combining CNN and Transformer models, achieving high accuracy on handwritten and printed texts, suitable for real-world applications.

Contribution

The paper introduces a novel Arabic OCR solution integrating CNN and Transformer architectures, addressing script variability and noise, with comprehensive detection and recognition capabilities.

Findings

81.66% precision in text detection

0.59% CER on printed text

7.91% CER on handwritten text

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.