Scene Text Detection with Supervised Pyramid Context Network

Pyramid Scene Parsing Network — Supplymentary Material

Aggregating Local Context for Accurate Scene Text Detection

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...

SEE: Towards Semi-Supervised End-to-End Scene Text Recognition

Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-t...

Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees

Maximally Stable Extremal Regions (MSERs) have achieved great success in scene text detection. However, this low-level pixel operation inherently limits its capability for handling complex text information efficiently (e. g. connections between text or background components), leading to the difficulty in distinguishing texts from background components. In this paper, we propose a novel framewor...

Scene Text Area Detection from Video

Text detection from videos is a well known research area. Especially the detection of static superimposed text such as captions has been researched successfully, but makes many assumptions that question the applicability of those algorithms for moving scene text. In this dissertation, I propose a scene text area detection approach that includes a simple key frame extraction, feature extraction,...