An introduction to digital image processing
An introduction to digital image processing
Pattern Recognition
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Comment on Using the Uniformity Measure for Performance Measure in Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction and recognition of artificial text in multimedia documents
Pattern Analysis & Applications
Image thresholding using Tsallis entropy
Pattern Recognition Letters
Thresholding technique with adaptive window selection for uneven lighting image
Pattern Recognition Letters
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
A Binarization Algorithm specialized on Document Images and Photos
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Adaptive Binarization of Historical Document Images
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
On minimum variance thresholding
Pattern Recognition Letters
OCR binarization and image pre-processing for searching historical documents
Pattern Recognition
Unsupervised performance evaluation of image segmentation
EURASIP Journal on Applied Signal Processing
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Neuro semantic thresholding using OCR software for high precision OCR applications
Image and Vision Computing
A multi-scale framework for adaptive binarization of degraded document images
Pattern Recognition
Dynamic Measurement of Computer Generated Image Segmentations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tsallis entropy and the long-range correlation in image thresholding
Signal Processing
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In this paper, we propose a mechanism for systematic comparison of the efficacy of unsupervised evaluation methods for parameter selection of binarization algorithms in optical character recognition (OCR). We also analyze these measures statistically and ascertain whether a measure is suitable or not to assess a binarization method. The comparison process is streamlined in several steps. Given an unsupervised measure and a binarization algorithm we: (i) find the best parameter combination for the algorithm in terms of the measure, (ii) use the best binarization of an image on an OCR, and (iii) evaluate the accuracy of the characters detected. We also propose a new unsupervised measure and a statistical test to compare measures based on an intuitive triad of possible results: better, worse or comparable performance. The comparison method and statistical tests can be easily generalized for new measures, binarization algorithms and even other accuracy-driven tasks in image processing. Finally, we perform an extensive comparison of several well known measures, binarization algorithms and OCRs, and use it to show the strengths of the WV measure.