Language segmentation for Optical Character Recognition using Self Organizing Maps
نویسنده
چکیده
Modern optical character recognition (OCR) systems perform optimally on single-font monolingual texts, and have lower performance on bilingual and multilingual texts. For many OCR tasks it is necessary to accurately recognize characters from bilingual texts such as dictionaries or grammar books. We present a novel approach to segmenting bilingual text, easily extensible to more than two languages. Our approach uses self organizing maps to distinguish between characters of different languages allowing OCR to be performed on each part separately.
منابع مشابه
Green Product Consumers Segmentation Using Self-Organizing Maps in Iran
This study aims to segment the market based on demographical, psychological, and behavioral variables, and seeks to investigate their relationship with green consumer behavior. In this research, self-organizing maps are used to segment and to determine the features of green consumer behavior. This was a survey type of research study in which eight variables were selected from the demographical,...
متن کاملTitle Handwritten Chinese character recognition using spatial Gabor filters and self-organizing feature maps
So far the bottleneck of Chinese character recognition, especially handwritten character recognition, still lies in the effectiveness of featureextraction to cater for various distortions and position shifting. In ths paper, a novel method is proposed by applying a set of Gabor spatial filters with different directions and spatial frequencies to character images, in an effort to reach the optim...
متن کاملSOM Segmentation of gray scale images for optical recognition
This paper describes a clustering technique using Self Organizing Maps and a two-dimensional histogram of the image. The two-dimensional histogram is found using the pixel value and the mean in the neighborhood. This histogram is fed to a self organizing map that divides the histogram into regions. Carefully selecting the number of regions, a scheme that allows an optimum optical recognition of...
متن کاملHandwritten Chinese Character Recognition Using Spatial Gabor Filters and Self-Organizing Feature Maps
So far the bottleneck of Chinese character recognition, especially handwritten character recognition, still lies in the effectiveness of featureextraction to cater for various distortions and position shifting. In ths paper, a novel method is proposed by applying a set of Gabor spatial filters with different directions and spatial frequencies to character images, in an effort to reach the optim...
متن کاملDigital Realisation of Self-Organizing Maps
Kevin J. Moon A digital realisation of two-dimensional self-organising feature maps is presented. The method is based on subspace classification using an n-tuple technique. Weight vector approximation and orthogonal projections to produce a winnertakes-all network are also discussed. Over one million effective binary weights can be applied in 25ms using a conventional microcomputer. Details of ...
متن کامل