Cursive character recognition by learning vector quantization
نویسندگان
چکیده
منابع مشابه
Cursive character recognition by learning vector quantization
This paper presents a cursive character recognizer embedded in an o-line cursive script recognition system. The recognizer is composed of two modules: the ®rst one is a feature extractor, the second one a learning vector quantizer. The selected feature set was compared to Zernike polynomials using the same classi®er. Experiments are reported on a database of about 49,000 isolated characters.
متن کاملCombining neural gas and learning vector quantization for cursive character recognition
This paper presents a cursive character recognizer, a crucial module in any Cursive Script Recognition system based on a segmentation and recognition approach. The character classi2cation is achieved by combining the use of neural gas (NG) and learning vector quantization (LVQ). NG is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not....
متن کاملStroke-Based Cursive Character Recognition
Off-line character recognition – Off-line character recognition takes a raster image from a scanner (scanned images of the paper documents), digital camera or other digital input sources. The image is binarised based on for instance, color pattern (color or gray scale) so that the image pixels are either 1 or 0. On-line character recognition – In on-line, the current information is presented to...
متن کاملChapter 0 Stroke - Based Cursive Character Recognition
In this chapter, we keep focusing on on-line writer independent cursive character recognition engine. In what follows, we explain the importance of on-line handwriting recognition over off-line, the necessity of writer independent system and the importance as well as scope of cursive scripts like Devanagari. Devanagari is considered as one of the known cursive scripts [20, 29]. However, we aim ...
متن کاملA Novel Vector Quantization Approach to Arabic Character Recognition
In this paper, a novel approach to Arabic letter recognition is proposed. The system is based on the classified vector quantization (CVQ) technique employing the minimum distance classifier. To prove the robustness of the CVQ system, its performance is compared to that of a standard artificial neural network (ANN)-based solution. In the CVQ system, each input letter is mapped to its class using...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition Letters
سال: 2001
ISSN: 0167-8655
DOI: 10.1016/s0167-8655(01)00008-3