Pattern Recognition
A new method for image segmentation
Computer Vision, Graphics, and Image Processing
Improvement of Kittler and Illingworth's minimum error thresholding
Pattern Recognition
Text segmentation using Gabor filters for automatic document processing
Machine Vision and Applications - Special issue: document image analysis techniques
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Character and line extraction from color map images using a multi-layer neural network
Pattern Recognition Letters
Evaluation of Binarization Methods for Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hopfield neural network for adaptive image segmentation: an active surface paradigm
Pattern Recognition Letters
DL '97 Proceedings of the second ACM international conference on Digital libraries
Low resolution, degraded document recognition using neural networks and hidden Markov models
Pattern Recognition Letters
A Statistical, Nonparametric Methodology for Document Degradation Model Validation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Gray-Scale Extraction of Features for Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Extraction of Topographic Features for Gray Scale Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video OCR for Digital News Archive
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
A Video Text Extraction Method for Character Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Robust Extraction of Text in Video
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Text Enhancement with Asymmetric Filter for Video OCR
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multistage adaptive thresholding method
Pattern Recognition Letters
Towards a Multinational Car License Plate Recognition System
Machine Vision and Applications
Low Resolution Character Recognition by Image Quality Evaluation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
OCR binarization and image pre-processing for searching historical documents
Pattern Recognition
A hidden Markov model-based character extraction method
Pattern Recognition
Recognition of degraded characters using dynamic Bayesian networks
Pattern Recognition
Digital image thresholding, based on topological stable-state
Pattern Recognition
A neuro-fuzzy inference engine for Farsi numeral characters recognition
Expert Systems with Applications: An International Journal
An adaptive technique for global and local skew correction in color documents
Expert Systems with Applications: An International Journal
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
Binarization of color document images via luminance and saturation color features
IEEE Transactions on Image Processing
A robust video text detection approach using SVM
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper text segmentation in generic displays is proposed through learning the best binarization values for a commercial optical character recognition (OCR) system. The commercial OCR is briefly introduced as well as the parameters that affect the binarization for improving the classification scores. The purpose of this work is to provide the capability to automatically evaluate standard textual display information, so that tasks that involve visual user verification can be performed without human intervention. The problem to be solved is to recognize text characters that appear on the display, as well as the color of the characters' foreground and background. The paper introduces how the thresholds are learnt through: (a) selecting lightness or hue component of a color input cell, (b) enhancing the bitmaps' quality, and (c) calculating the segmentation threshold range for this cell. Then, starting from the threshold ranges learnt at each display cell, the best threshold for each cell is gotten. The input and output data sets for testing the algorithms proposed are described, as well as the analysis of the results obtained.