Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded character structures, and cluttered background part images. Affected by these factors, bounding boxes generated most existing methods locate low-contrast text areas inaccurately. In this paper, we propose a refined feature-attentive network (RFN) solve inaccur...

We describe a pc−based low cost visual system that can detect and extract text regions in visual signs in the scene and recognize them for location awareness. It employs a multi resolution image enhancement and segmentation methods based on symmetric neighborhood filter and hierarchical connected component analysis to extract written information on signboards which appears in the scene. The mul...

Personalized graphical user interfaces have the potential to reduce visual complexity and improve interaction efficiency by tailoring elements such as menus and toolbars to better suit an individual user's needs. When an interface is personalized to make useful features more accessible for a user's current task, however, there may be a negative impact on the user's awareness of the full set of ...

Feature subset selection has become more and more a common topic of research. This popularity is partly due to the growth in the number of features and application domains. It is of the greatest importance to take the most of every evaluation of the inducer, which is normally the more costly part. In this paper, a technique is proposed that takes into account the inducer evaluation both in the ...

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, de...

Recent years, feature selection is chief concern in text classification. A major characteristic in text classification is the high dimensionality of the feature space. Therefore, feature selection is strongly considered as one of the crucial part in text document categorization. Selecting the best features to represent documents can reduce the dimensionality of feature space hence increase the ...

In order to improve the accuracy of short text similarity calculation, this paper presents the idea that use the history of short text messages to construct semantic feature space, then use the vector in semantic feature space to represent short text and do semantic extension, and finally calculate the short text similarity of corresponding vector in the semantic feature space. This method can ...

This paper describes an intelligent text archive system in which typed feature structures are embedded. The aim of the system is to associate feature structures with regions in text, to make indexes for efficient retrieval, to allow users to specify both structure and proximity, and to enable inference on typed feature structures embedded in text. We propose a persistent mechanism for storing t...

We present a new context-aware method for lexical simplification that uses two free language resources and real web frequencies. We compare it with the state-of-the-art method for lexical simplification in Spanish and the established simplification baseline, that is, the most frequent synonym. Our method improves upon the other methods in the detection of complex words, in meaning preservation,...