On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction and recognition of artificial text in multimedia documents
Pattern Analysis & Applications
Object count/area graphs for the evaluation of object detection and segmentation algorithms
International Journal on Document Analysis and Recognition
Color text extraction with selective metric-based clustering
Computer Vision and Image Understanding
A Robust System to Detect and Localize Texts in Natural Scene Images
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Text Localization in Natural Scene Images Based on Conditional Random Field
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Support vector machine experiments for road recognition in high resolution images
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
Service robots could use textual information to perform important tasks, like product identification. However, natural scene text such as found in household environments can be very arbitrary in terms of size, color, font, layout, symbol repertoire, language, etc. This large variability makes robust text information extraction extremely difficult. Our work on textual information extraction for gray-scale still images uses adaptive binarization, connected component classification with a support vector machine and filtering based on the proximity of the connected components to their neighbours. The contribution of our approach is the use of a partially synthetic dataset for training. This decreases the burden of ground truth labelling at the connected component level. Our experiments show that classification generalization on real instances can be attained when training a classifier with synthetic data. We present our results on the ICDAR dataset.