Rotation-invariant features for multi-oriented text detection in natural images.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes

Text Extraction System From High As Well As Low Resolution Natural Scene Images

In this paper, we propose efficient and sturdy technique for investigating texts in natural scene footage. A fast and effective pruning formula is designed to extract Maximally Stable External Regions (MSERs) as character candidate’s victimization the strategy of minimizing regularized variations. Character candidates form into text candidates by the single-link clump formula, wherever distance weights and clump threshold unit of measurement learned by a completely distinctive self-training distance metric learning formula. The probabilities of text candidates like non-text unit of measurement estimable with a temperament classifier. Text candidates with high non-text probabilities unit of density eliminated and texts unit of measurement acknowledged with a document classifier. Text find in natural scene footage is also an important for several content-based image resolve. Experiments on polyglot, street browse; multi-direction and even born-digital databases conjointly demonstrate the effectiveness of the reposed technique