With the increasing use of digital image capturing devices, such as digital cameras, mobile phones and PDAs, content-based image analysis techniques are receiving intensive attention in recent years. Among all the contents in images, text information has inspired great interests, since it can be easily understood by both human and computer, and leads to wide applications. An integral Text Information Extraction (TIE) system contains four parts: text detection, text localization, text extraction and OCR. Thereinto, the first two parts text detection and localization are very important for the system performance. Based on these backgrounds, this paper aims to give a thorough research on the method of scene text detection and localization by utilizing pattern recognition, image processing and machine learning techniques. This paper presents three new text detection and localization methods, while the experimental results show their superiorities compared with other existing state-of-the-art methods: Considering the textual varieties of text regions and system speed requirement, we present a robust system to accurately detect and localize texts in natural scene images. Experiments on the public Dataset show that our system is comparable to the best existing methods both in accuracy and speed. To take advantages of both region-based and component-based information, we present a hybrid approach for robust scene text detection and localization. Experimental results show that our approach has better precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image database with promising results. To speed up the text detection process for closing to practical usage, we propose a fast scene text localization method by combining learning-based region filtering and verification in a coarse-to-fine strategy. Experimental results show that the proposed method provides competitive localization performance at high speed.