Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …

This paper analyzes, compares, and contrasts technical challenges, methods, and the
performance of text detection and recognition research in color imagery. It summarizes the …

This paper introduces a novel rotation-based framework for arbitrary-oriented text detection
in natural scene images. We present the Rotation Region Proposal Networks, which are …

Incidental scene text spotting is considered one of the most difficult and valuable challenges
in the document analysis community. Most existing methods treat text detection and …

In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for
detecting arbitrary-oriented texts in natural scene images. The framework is based on Faster …

Text in curve orientation, despite being one of the common text orientations in real world
environment, has close to zero existence in well received scene text datasets such as …

In this paper we introduce a new method for text detection in natural images. The method
comprises two contributions: First, a fast and scalable engine to generate synthetic images …

In this paper, we propose an unconventional approach for text detection in natural images.
Both global and local cues are taken into account for localizing text lines in a coarse-to-fine …

In this work we present an end-to-end system for text spotting—localising and recognising
text in natural scene images—and text based image retrieval. This system is based on a …

The focus of this paper is speeding up the evaluation of convolutional neural networks.
While delivering impressive results across a range of computer vision and machine learning …