Invizo: Arabic Handwritten Document Optical Character Recognition Solution

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.

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

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.