Caption Localisation in Video Sequences by Fusion of Multiple Detectors
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Detecting text in video frames
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Fast and robust text detection in images and video frames
Image and Vision Computing
Object detection using spatial histogram features
Image and Vision Computing
Detecting text in video frames
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
A two-stage scheme for text detection in video images
Image and Vision Computing
Text detection in images using sparse representation with discriminative dictionaries
Image and Vision Computing
Evaluation of commercial OCR: a new goal directed methodology for video documents
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Using adaptive run length smoothing algorithm for accurate text localization in images
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A framework for improved video text detection and recognition
Multimedia Tools and Applications
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Text presented in videos provides important supplemental information for video indexing and retrieval. Many efforts have been made for text detection in videos. However, there is still a lack of performance evaluation protocols for video text detection. In this paper, we propose an objective and comprehensive performance evaluation protocol for video text detection algorithms. The protocol includes a positive set and a negative set of indices at the textbox level, which evaluate the detection quality in terms of both location accuracy and fragmentation of the detected textboxes. In the protocol, we assign a detection difficulty (DD) level to each ground truth textbox. The performance indices can then be normalized with respect to the textbox DD level and are therefore tolerant to different ground-truth difficulties to a certain degree. We also assign a detectability index (DI) value to each ground-truth textbox. The overall detection rate is the DI-weighted average of the detection qualities of all ground-truth textboxes, which makes the detection rate more accurate to reveal the real performance. The automatic performance evaluation scheme has been applied to performance evaluation of a text detection approach to determine the best thresholds that can yield the best detection results. The protocol has also been employed to compare the performances of several text detection systems. Hence, we believe that the proposed protocol can be used to compare the performance of different video/image text detection algorithms/systems and can even help improve, select, and design new text detection methods.