Word segmentation is the most critical pre-processing step for any handwritten document recognition and/or
retrieval system. When the writing style is unconstrained (written in a natural manner), recognition of individual
components may be unreliable, so they must be grouped together into word hypotheses before recognition
algorithms can be used. This paper describes a gap metrics based machine learning approach to separate a line
of unconstrained handwritten text into words. Our approach uses a set of both local and global features, which
is motivated by the ways in which human beings perform this kind of task. In addition, in order to overcome
the disadvantage of different distance computation methods, we propose a combined distance measure computed
using three different methods. The classification is done by using a three-layer neural network. The algorithm is
evaluated using an unconstrained handwriting database that contains 50 pages (1026 line, 7562 words images)
handwritten documents. The overall accuracy is 90.8%, which shows a better performance than a previous
method.
Search aspects of a system for analyzing handwritten documents are described. Documents are indexed using global image features, e.g., stroke width, slant as well as local features that describe the shapes of words and characters. Image indexing is done automatically using page analysis, page segmentation, line separation, word segmentation and recognition of words and characters. Two types of search are permitted: search based on global features of entire document and search using features at local level. For the second type of search, i.e., local, all the words in the document are characterized and indexed by various features and it forms the basis of different search techniques. The paper focuses on local search and describes four tasks: word/phrase spotting, text to image, image to text and plain text. Performance in terms of precision/recall and word ranking is reported on a database of handwriting samples from about 1,000 individuals.
Existing word image retrieval algorithms suffer from either low retrieval precision or high computation complexity. We present an effective and efficient approach for word image matching by using gradient-based binary features. Experiments over a large database of handwritten word images show that the proposed approach consistently outperforms the existing best handwritten word image retrieval algorithm. Dynamic Time Warping (DTW) with profile-based shape features. Not only does the proposed approach have much higher retrieval accuracy, but also it is 893 times faster than DTW.
KEYWORDS: Iterated function systems, Fractal analysis, Image processing, 3D modeling, Visual process modeling, Computer graphics, Electronics, Electronics engineering, Process control, Data modeling
A novel 3D-terrain construction algorithm is represented in this paper. Traditional construction algorithms based on terrain self-similitude often ignore its structural features. This algorithm not only guarantees the integral and local self-similitude of constructed terrain, but also describes its structural features realistically. Iterated function system is used to construct terrain structural lines first. Then heights are got and adjusted by washing- out algorithm. Structural features of constructed 3D-terrain with this algorithm is conforming to the natural scenes, so it is more similar to natural scenes.
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