Text detection in images of natural scenes is important for scene understanding, content-based image analysis, assistive navigation and automatic geocoding. Achieving such text detection is challenging due to complex backgrounds, non-uniform illumination, and variations in text font, size, and orientation. In this paper, we present a novel hybrid approach for detecting text robustly in natural ...

This chapter presents two basic processes needed in a vision system for a mobile robot which is capable of reading the textual content encountered in its environment. We describe the general system outline and we present the structure and the experimental evaluation of the system modules that we built until the present. These two modules are designed to solve two essential problems facing our r...

In this paper we propose an edge-based algorithm for artificial text detection in video frames. First, an edge map is created using the Canny edge detector. Then, morphological filtering is used, based on geometrical constraints, in order to connect the vertical edges and discard false alarms. A connected component analysis is performed to the filtered edge map in order to determine a bounding ...

AdaBoost has been verified to be proficient in processing images rapidly while attaining high detection rate in face detection. The speed of AdaBoost in face detection is demonstrated in [1], where the detection can be performed in 15 frames per second. The robust speediness and the high accuracy in tracing the target objects have enable AdaBoost to be successful in classification problems. In ...

Video texts are closely related to the content of a video. They provide a valuable source for indexing and interpretation of video data. Text detection and recognition task in images or videos typically distinguished between overlay and scene text. Overlay text is artificially superimposed on the image at the time of editing and scene text is text captured by the recording system. Typically, OC...

Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this wor...

Detecting text reuse is a fundamental requirement for a variety of tasks and applications, ranging from journalistic text reuse to plagiarism detection. Text reuse is traditionally detected by computing similarity between a source text and a possibly reused text. However, existing text similarity measures exhibit a major limitation: They compute similarity only on features which can be derived ...

Video is one of the sources for presenting the valuable information. It contains sequence of video images, audio and text information. Text data present in video contain useful information for automatic annotation, structuring, mining, indexing and retrieval of video. Nowadays mechanically added (superimposed) text in video sequences provides useful information about their contents. It provides...

Scene text reading continues to be of interest for many reasons including applications for the visually impaired and automatic image indexing systems. Here we propose a novel end-to-end scene text detection algorithm. First, for identifying text regions we design a novel Convolutional Neural Network (CNN) architecture that aggregates local surrounding information for cascaded, fast and accurate...