Тасвирлардан турли шаклдаги матнни аниқлашнинг сунъий интеллектга асосланган ечимлари

This article explores the achievements and methodologies of artificial intelligence (AI) for text recognition from images, focusing on optical character recognition (OCR) and its AI-driven advanced approaches. It examines various AI models, such as deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), which enhance accuracy and flexibility in recognizing different text forms. The article discusses the challenges these systems face, such as text in low-quality images, various fonts, languages, and complex layouts. Specifically, it highlights AI methods that address these challenges, including pre-trained models, adaptive learning, and real-time processing improvements. Through case studies, it demonstrates applications in areas such as document digitization, automated translation, and data extraction. Furthermore, the article evaluates the effectiveness of hybrid AI models that combine traditional OCR with machine learning to ensure high accuracy rates. The future research directions in this field are presented, emphasizing AI’s role in making text recognition more accessible, scalable, and applicable across different domains. This work provides insights into the practical application and transformative potential of AI in visual text recognition, laying the foundation for future innovations in automated data extraction.