Integral ratio: a new class of global thresholding techniques for handwriting images
Yan Solihin, C.G. Leedham · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1999
We propose a class of histogram based global thresholding techniques called integral ratio. They are designed to threshold gray-scale handwriting images and separate the handwriting from the background. The following tight requirements must be met: 1) all the details of the handwriting are to be retained, 2) the writing paper used may contain strong colored and/or patterned background which must be removed, and 3) the handwriting may be written using a wide variety of pens such as a fountain pen, ballpoint pen, or pencil. A specific application area which requires these tight requirements is forensic document examination, where a handwritten document is often considered as legal evidence and the handwriting must not be tampered with or modified in any way. The proposed class of techniques is based on a two stage thresholding approach requiring each pixel of a handwritten image to be placed into one of three classes: foreground, background, and a fuzzy area between them where it is hard to determine whether a pixel belongs to the foreground or the background. Two techniques, native integral ratio (NIR) and quadratic integral ratio (QIR), were created based on this class and tested against two well-known thresholding techniques: Otsu's (1979) technique and the entropy thresholding technique. We found that QIR has superior performance compared to all the other techniques tested.