We propose a novel method by using three new character features to detect text objects comprising two or more isolated characters in images and videos. A new text model is constructed to describe text objects. Each character is a part in the model and every two neighboring characters are connected by a link. Two characters and the link connecting them are defined as a text unit. For every candidate part, we compute character energy based on our observation that each character stroke forms two edges with high similarities in length, curvature, and orientation. For every candidate link, we compute link energy based on the similarities in color, size, stroke width, and spacing between characters that are aligned along a particular direction. For every candidate text unit, we combine character and link energies to compute text unit energy which measures the likelihood that the candidate is a text object. We evaluated the performance of the proposed method on ICDAR 2003/2005 dataset, Microsoft Street view dataset, and VACE video dataset. The experimental results demonstrate that our method can capture the inherent properties of characters and discriminate text from other objects effectively.

Shape analysis is an active and important branch in computer vision research field. In recent years, many geometrical, topological, and statistical features have been proposed and widely used for shape-related applications. In this paper, based on the properties of Distance Transform, we present a new shape feature, weight of boundary point. By computing the shortest distances between boundary points and distance contours of a transformed shape, every boundary point is assigned a weight, which contains the interior structure information of the shape. To evaluate the proposed new shape feature, we tested the weighted boundary points on shape matching and shape decomposition. The experimental results demonstrated the validity.

In this paper, we propose a new unsupervised text detection approach which is based on Histogram of Oriented Gradient and Graph Spectrum. By investigating the properties of text edges, the proposed approach first extracts text edges from an image and localize candidate character blocks using Histogram of Oriented Gradients, then Graph Spectrum is utilized to capture global relationship among candidate blocks and cluster candidate blocks into groups to generate bounding boxes of text objects in the image. The proposed method is robust to the color and size of text. ICDAR 2003 text locating dataset and video frames were used to evaluate the performance of the proposed approach. Experimental results demonstrated the validity of our approach.

Common benchmark data sets, standardized performance metrics, and baseline algorithms have demonstrated considerable impact on research and development in a variety of application domains. These resources provide both consumers and developers of technology with a common framework to objectively compare the performance of different algorithms and algorithmic improvements. In this paper, we present such a framework for evaluating object detection and tracking in video: specifically for face, text, and vehicle objects. This framework includes the source video data, ground-truth annotations (along with guidelines for annotation), performance metrics, evaluation protocols, and tools including scoring software and baseline algorithms. For each detection and tracking task and supported domain, we developed a 50-clip training set and a 50-clip test set. Each data clip is approximately 2.5 minutes long and has been completely spatially/temporally annotated at the I-frame level. Each task/domain, therefore, has an associated annotated corpus of approximately 450,000 frames. The scope of such annotation is unprecedented and was designed to begin to support the necessary quantities of data for robust machine learning approaches, as well as a statistically significant comparison of the performance of algorithms. The goal of this work was to systematically address the challenges of object detection and tracking through a common evaluation framework that permits a meaningful objective comparison of techniques, provides the research community with sufficient data for the exploration of automatic modeling techniques, encourages the incorporation of objective evaluation into the development process, and contributes useful lasting resources of a scale and magnitude that will prove to be extremely useful to the computer vision research community for years to come.

Text extraction in video documents, as an important research field of content-based information indexing and retrieval, has been developing rapidly since 1990s. This has led to much progress in text extraction, performance evaluation, and related applications. By reviewing the approaches proposed during the past five years, this paper introduces the progress made in this area and discusses promising directions for future research.

In this paper, we propose a new edge-based text verification approach for video. Based on the investigation of the relation between candidate blocks and their neighbor areas, the proposed approach first detects background edges in candidate blocks, then erases them by an edge tracking technique, and finally the candidate blocks containing too few remaining edges are eliminated as false alarms. Three measures for text detection evaluation in video were used to assess the performance of the proposed text verification approach. Experimental results on 50 broadcast news video clips demonstrate the validity of our approach.