Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Address block location using color and texture analysis
CVGIP: Image Understanding
Neural network-based text location in color images
Pattern Recognition Letters
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Hybrid approach to efficient text extraction in complex color images
Pattern Recognition Letters
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Finding Text in Natural Scenes by Figure-Ground Segmentation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
An Overview of the Tesseract OCR Engine
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Scene Text Extraction with Edge Constraint and Text Collinearity
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Automatic vehicle identification for Argentinean license plates using intelligent template matching
Pattern Recognition Letters
A Hybrid Approach to Detect and Localize Texts in Natural Scene Images
IEEE Transactions on Image Processing
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We present a new approach for automatic gas meter reading from real world images. The gas meter reading is usually done on site by an operator and a picture is taken from a mobile device as proof of reading. Since the reading operation is prone to errors, the proof image is checked offline by another operator to confirm the reading. In this study, we present a method to support the validation process in order to reduce the human effort. Our approach is trained to detect and recognize the text of a particular area of interest. Firstly we detect the region of interest and segment the text contained using a method based on an ensemble of neural models. Then we perform an optical character recognition using a Support Vector Machine. We evaluated every step of our approach, as well as the overall assessment, showing that despite the complexity of the problem our method provide good results also when applied to degraded images and can therefore be used in real applications.