🚀 Building a High-Accuracy Arabic OCR Tool: How I Solved the "Image-to-Text" Challenge

Extraction of text from images (OCR) is a solved problem for Latin languages, but for Arabic, it’s a whole different story. As the developer behind Adawati.app , I spent weeks engineering a solution that doesn't just "read" Arabic, but understands its complexity. The Problem: Why Arabic OCR is Hard Most open-source OCR engines struggle with Arabic for three reasons: Cursive Nature: Arabic letters change shape based on their position (Start, Middle, End). Diacritics & Dots: Small dots and marks can change the entire meaning of a word. Low-Quality Input: Students often take photos of textbooks in poor lighting or at weird angles. My Engineering Approach Instead of just "plugging in" a generic API, I built a pipeline focused on Pre-processing and Contextual Inference. Image Pre-processing (The Secret Sauce) Before the AI even looks at the image, I apply several filters: Binarization: Converting the image to high-contrast black and white to eliminate background noise. Deskewing: Automatica