392 research outputs found
The Robust Reading Competition Annotation and Evaluation Platform
The ICDAR Robust Reading Competition (RRC), initiated in 2003 and
re-established in 2011, has become a de-facto evaluation standard for robust
reading systems and algorithms. Concurrent with its second incarnation in 2011,
a continuous effort started to develop an on-line framework to facilitate the
hosting and management of competitions. This paper outlines the Robust Reading
Competition Annotation and Evaluation Platform, the backbone of the
competitions. The RRC Annotation and Evaluation Platform is a modular
framework, fully accessible through on-line interfaces. It comprises a
collection of tools and services for managing all processes involved with
defining and evaluating a research task, from dataset definition to annotation
management, evaluation specification and results analysis. Although the
framework has been designed with robust reading research in mind, many of the
provided tools are generic by design. All aspects of the RRC Annotation and
Evaluation Framework are available for research use.Comment: 6 pages, accepted to DAS 201
Efficient Scene Text Localization and Recognition with Local Character Refinement
An unconstrained end-to-end text localization and recognition method is
presented. The method detects initial text hypothesis in a single pass by an
efficient region-based method and subsequently refines the text hypothesis
using a more robust local text model, which deviates from the common assumption
of region-based methods that all characters are detected as connected
components.
Additionally, a novel feature based on character stroke area estimation is
introduced. The feature is efficiently computed from a region distance map, it
is invariant to scaling and rotations and allows to efficiently detect text
regions regardless of what portion of text they capture.
The method runs in real time and achieves state-of-the-art text localization
and recognition results on the ICDAR 2013 Robust Reading dataset
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
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