Integral Ratio: A New Class of Global Thresholding Techniques for Handwriting Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Character Extraction from Noisy Background for an Automatic Reference System
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A noise attribute thresholding method for document image binarization
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Classification of Hebrew Calligraphic Handwriting Styles: Preliminary Results
DIAL '04 Proceedings of the First International Workshop on Document Image Analysis for Libraries (DIAL'04)
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Statistical mixture model for documents skew angle estimation
Pattern Recognition Letters
| Hi-index | 0.10 |
In this paper, we describe an input sensitive thresholding algorithm for ancient Hebrew calligraphy documents. Usually, historical document images are of poor quality since the documents have degraded over time due to storage conditions. However, the distribution of noise in one document is not uniform and the characters quality may vary. We develop tools to identify noisy characters and apply more sophisticated tools to process them. First, we use a global thresholding method to obtain an initial binary image. This suffices for noise free characters. Then we evaluate the document characters and invoke an accurate local method only on the noisy characters. Results show that our method detects a very high percent of the noisy characters, and that the local method achieves very accurate results.