Usage-oriented multimedia information retrieval technological evaluation
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
A two-stage scheme for text detection in video images
Image and Vision Computing
A framework for the assessment of text extraction algorithms on complex colour images
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Evaluation metric for image understanding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
On the detection of textual information in metro stations
Proceedings of the 7th International Conference on Frontiers of Information Technology
MAST: multi-script annotation toolkit for scenic text
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Johnny: An Autonomous Service Robot for Domestic Environments
Journal of Intelligent and Robotic Systems
Parametrization of an image understanding quality metric with a subjective evaluation
Pattern Recognition Letters
A text reading algorithm for natural images
Image and Vision Computing
Hybrid POMDP based evolutionary adaptive framework for efficient visual tracking algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Multi-script robust reading competition in ICDAR 2013
Proceedings of the 4th International Workshop on Multilingual OCR
Integrating multiple character proposals for robust scene text extraction
Image and Vision Computing
Learning to discriminate text from synthetic data
Robot Soccer World Cup XV
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Evaluation of object detection algorithms is a non-trivial task: a detection result is usually evaluated by comparing the bounding box of the detected object with the bounding box of the ground truth object. The commonly used precision and recall measures are computed from the overlap area of these two rectangles. However, these measures have several drawbacks: they don't give intuitive information about the proportion of the correctly detected objects and the number of false alarms, and they cannot be accumulated across multiple images without creating ambiguity in their interpretation. Furthermore, quantitative and qualitative evaluation is often mixed resulting in ambiguous measures.In this paper we propose a new approach which tackles these problems. The performance of a detection algorithm is illustrated intuitively by performance graphs which present object level precision and recall depending on constraints on detection quality. In order to compare different detection algorithms, a representative single performance value is computed from the graphs. The influence of the test database on the detection performance is illustrated by performance/generality graphs. The evaluation method can be applied to different types of object detection algorithms. It has been tested on different text detection algorithms, among which are the participants of the ICDAR 2003 text detection competition.