Search of Method for Analyzing "Viability" of Innovative Projects

Questions of "viability" evaluation of innovation projects are considered in this article. As a method of evaluation Hidden Markov Models are used. Problem of determining model parameters, which reproduce test data with highest accuracy are solving. For training the model statistical data on the implementation of innovative projects are used. Baum-Welch algorithm is used as a training algorithm

Arabic Handwriting Synthesis

Training and testing data for optical character recognition are cumbersome to obtain. If large amounts of data can be produced from small amounts, much time and effort can be saved. This paper presents an approach to synthesize Arabic handwriting. We segment word images into labeled characters and then use these in synthesizing arbitrary words. The synthesized text should look natural; hence, we define some criteria to decide on what is acceptable as natural-looking. The text that is synthesized by using the natural-looking constrain is compared to text that is synthesized without using the natural-looking constrain for evaluation

Application of the Hidden Markov Model for Innovative Projects "Viability" Analysis

This master thesis deals with determining of innovative projects "viability". "Viability" is the probability of innovative project being implemented. Hidden Markov Models are used for evaluation of this factor. The problem of determining parameters of model, which produce given data sequence with the highest probability, are solving in this research. Data about innovative projects contained in reports of Russian programs "UMNIK", "START" and additional data obtained during study are used as input data for determining of model parameters. The Baum-Welch algorithm which is one implementation of expectation-maximization algorithm is used at this research for calculating model parameters. At the end part of the master thesis mathematical basics for practical implementation are given (in particular mathematical description of the algorithm and implementation methods for Markov models)

The advantage of using an HMM-based approach for faxed word recognition

A method for word recognition based on the use of hidden Markov models (HMMs) is described. An evaluation of its performance is presented using a test set of real printed documents that have been subjected to severe photocopy and fax transmission distortions. A comparison with a commercial OCR package highlights the inherent advantages of a segmentation-free recognition strategy when the word images are severely distorted, as well as the importance of using contextual knowledge. The HMM method makes only one quarter of the number of word errors made by the commercial package when tested on word images taken from faxed pages. © 1998 Springer-Verlag Berlin Heidelberg

The advantage of using an HMM-based approach for faxed word recognition

A method for word recognition based on the use of hidden Markov models (HMMs) is described. An evaluation of its performance is presented using a test set of real printed documents that have been subjected to severe photocopy and fax transmission distortions. A comparison with a commercial OCR package highlights the inherent advantages of a segmentation-free recognition strategy when the word images are severely distorted, as well as the importance of using contextual knowledge. The HMM method makes only one quarter of the number of word errors made by the commercial package when tested on word images taken from faxed pages. © 1998 Springer-Verlag Berlin Heidelberg