A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive degraded document image binarization
Pattern Recognition
OCR binarization and image pre-processing for searching historical documents
Pattern Recognition
Text line detection in handwritten documents
Pattern Recognition
A Modified Adaptive Logical Level Binarization Technique for Historical Document Images
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Neuro semantic thresholding using OCR software for high precision OCR applications
Image and Vision Computing
Document image binarization using background estimation and stroke edges
International Journal on Document Analysis and Recognition
Transition Thresholds for Binarization of Historical Documents
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
H-DIBCO 2010 - Handwritten Document Image Binarization Competition
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
DIBCO 2009: document image binarization contest
International Journal on Document Analysis and Recognition - Special Issue on Performance Evaluation
Writer identification using directional ink-trace width measurements
Pattern Recognition
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
An improved contour-based thinning method for character images
Pattern Recognition Letters
ICDAR 2011 Document Image Binarization Contest (DIBCO 2011)
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
New Binarization Approach Based on Text Block Extraction
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Using a boosted tree classifier for text segmentation in hand-annotated documents
Pattern Recognition Letters
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In this article, we introduce a novel technique to remove binary artifacts. Given a gray-intensity image and its corresponding binary image, our method detects and remove connected components that are more likely to be background pixels. With this aim, our method constructs an auxiliary image by the minimum-error-rate threshold and, then, computes the ratio of intersection between the connected components of the original binary image and the connected components of the auxiliary image. Connected components with high ratio are considered true connected components while the rest are removed from the output. We tested our method in binarization methods for historical documents (handwritten and printed). Our results are favorable and indicate that our method can improve the outputs from diverse binarization methods. In particular, a high improvement was observed for printed documents. Our method is easy to implement, has a moderate computational cost, and has two parameters whose model interpretation allows an easy empirical selection.