Analysis of Text Detection and Extraction Using Deep Learning and SVMs

Text detection and extraction from natural scenes and scanned documents is a critical task in computer vision, enabling numerous applications such as automated document digitization, real-time translation, and intelligent surveillance. Traditional Optical Character Recognition (OCR) methods often struggle with noisy, distorted, or cluttered backgrounds. To address these challenges, this review explores a hybrid approach that integrates Convolutional Neural Networks (CNNs) for robust text region detection and Support Vector Machines (SVMs) for accurate character classification. CNNs excel at learning spatial hierarchies from visual data, allowing effective localization of text under diverse conditions. Meanwhile, SVMs offer reliable performance in classifying individual characters, especially with limited training data and high-dimensional feature sets. The review highlights recent advancements, practical implementations, and the synergy of combining deep and classical machine learning methods. It concludes that the CNN-SVM hybrid model significantly improves text recognition accuracy in real-world scenarios, making it a promising solution for next-generation OCR systems.