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

ОБНАРУЖЕНИЕ ФРАГМЕНТОВ ТЕКСТА НА ИЗОБРАЖЕНИЯХ РЕАЛЬНЫХ СЦЕН НА БАЗЕ СВЕРТОЧНОЙ НЕЙРОСЕТЕВОЙ МОДЕЛИ

A model of text image detector based on a convolutional neural network architecture is presented, capable of synthesizing high-level features of images in the «black box» mode. An implementation of the detector application, based on algorithms of multi-scale scanning and local responses interpretation is described, allowing to find out text samples on images of real scenes. Advantages in comparison with analogs are shown and efficiency evaluation on an example of a known database is conducted.Рассматривается модель детектора текстовых образов на базе сверточной нейронной сети, способной синтезировать высокоуровневые признаки образов в режиме «черного ящика». Описывается методика применения детектора, основанная на алгоритмах мультимасштабного сканирования и локальной интерпретации откликов, позволяющая обнаруживать текстовые объекты на изображениях реальных сцен. Показываются преимущества разработок в сравнении аналогами, выполняется оценка эффективности на примере известной базы данных

An improved edge profile based method for text detection in images of natural scenes

Text detection in natural images has gained much attention in the last years as it is a primary step towards fully autonomous text recognition. Understanding the visual text content is of a vital importance in many applicative areas from the internet search engines to the PDA signboard translators. Images of natural scenes, however, pose numerous difficulties compared to the traditional scanned documents. They mainly contain diverse complex text of different sizes, styles and colors with complex backgrounds. Furthermore, such images are captured under variable lighting conditions and are often affected by the skew distortion and perspective projections. In this article an improved edge profile based text detection method is presented. It uses a set of heuristic rules to eliminate detection of non-text areas. The method is evaluated on CVL OCR DB, an annotated image database of text in natural scenes

An improved edge profile based method for text detection in images of natural scenes

Text detection in natural images has gained much attention in the last years as it is a primary step towards fully autonomous text recognition. Understanding the visual text content is of a vital importance in many applicative areas from the internet search engines to the PDA signboard translators. Images of natural scenes, however, pose numerous difficulties compared to the traditional scanned documents. They mainly contain diverse complex text of different sizes, styles and colors with complex backgrounds. Furthermore, such images are captured under variable lighting conditions and are often affected by the skew distortion and perspective projections. In this article an improved edge profile based text detection method is presented. It uses a set of heuristic rules to eliminate detection of non-text areas. The method is evaluated on CVL OCR DB, an annotated image database of text in natural scenes

An improved edge profile based method for text detection in images of natural scenes

Text detection in natural images has gained much attention in the last years as it is a primary step towards fully autonomous text recognition. Understanding the visual text content is of a vital importance in many applicative areas from the internet search engines to the PDA signboard translators. Images of natural scenes, however, pose numerous difficulties compared to the traditional scanned documents. They mainly contain diverse complex text of different sizes, styles and colors with complex backgrounds. Furthermore, such images are captured under variable lighting conditions and are often affected by the skew distortion and perspective projections. In this article an improved edge profile based text detection method is presented. It uses a set of heuristic rules to eliminate detection of non-text areas. The method is evaluated on CVL OCR DB, an annotated image database of text in natural scenes