Combined cosine-linear regression model similarity with application to handwritten word spotting

The similarity or the distance measure have been used widely to calculate the similarity or dissimilarity between vector sequences, where the document images similarity is known as the domain that dealing with image information and both similarity/distance has been an important role for matching and pattern recognition. There are several types of similarity measure, we cover in this paper the survey of various distance measures used in the images matching and we explain the limitations associated with the existing distances. Then, we introduce the concept of the floating distance which describes the variation of the threshold’s selection for each word in decision making process, based on a combination of Linear Regression and cosine distance. Experiments are carried out on a handwritten Arabic image documents of Gallica library. These experiments show that the proposed floating distance outperforms the traditional distance in word spotting system

Structured redefinition of sound units by merging and splitting for improved speech recognition

The performance of speech recognition systems degrades when the basic sound units used are poorly defined or inconsistently used. Several attempts have been made to improve dictionaries automatically, either by redefining pronunciations of words in terms of existing sound units, or by redefining the sound units themselves completely. The problem with these approaches is that, while the former is limited by the sound units used, the latter discards all human information that has been incorporated into an expert-designed recognition dictionary. In this paper we propose a new merging-andsplitting algorithm that attempts to redefine the basic sound units used in the dictionary, while maintaining the expert knowledge built into a manually designed dictionary. Sound units from an existing dictionary are merged based on their inherent confusability, as measured by a Monte-Carlo based metric, and subsequently split to maximize the likelihood of the training data. Experiments with the Resource Management database indicate that this approach results in an improvement in recognition accuracy when context-independent models are used for recognition. When context-dependent models are used, the improvement observed is reduced. 1