Name
Abstract | ||
|---|---|---|
A new analytic scheme, which uses a sequence of image segmentation and recognition algorithms, is proposed for the off-line cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, stroke width and height, are estimated. Second, a segmentation method finds character segmentation paths by combining gray-scale and binary information. Third, a hidden Markov model (HMM) is employed for shape recognition to label and rank the character candidates. For this purpose, a string of codes is extracted from each segment to represent the character candidates. The estimation of feature space parameters is embedded in the HMM training stage together with the estimation of the HMM model parameters. Finally, information from a lexicon and from the HMM ranks is combined in a graph optimization problem for word-level recognition. This method corrects most of the errors produced by the segmentation and HMM ranking stages by maximizing an information measure in an efficient graph search algorithm. The experiments indicate higher recognition rates compared to the available methods reported in the literature |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/TPAMI.2002.1008386 | Pattern Analysis and Machine Intelligence, IEEE Transactions |
Keywords | Field | DocType |
error correction,feature extraction,graph theory,handwritten character recognition,hidden Markov models,image segmentation,optical character recognition,optimisation,parameter estimation,search problems,HMM model parameters,HMM ranks,HMM training stage,analytic scheme,baselines,binary information,character candidate labelling,character candidate ranking,character segmentation paths,code string extraction,error correction,feature space parameters,global parameter estimation,graph optimization problem,graph search algorithm,gray-scale information,handwritten word recognition,hidden Markov model,image recognition algorithms,image segmentation algorithms,information measure maximization,lexicon matching,offline cursive handwriting recognition,optical character recognition,pre-processing,recognition rates,shape recognition,slant angle,stroke height,stroke width,word-level recognition | Graph theory,Feature vector,Graph traversal,Pattern recognition,Computer science,Segmentation,Optical character recognition,Feature extraction,Image segmentation,Speech recognition,Artificial intelligence,Hidden Markov model | Journal |
Volume | Issue | ISSN |
24 | 6 | 0162-8828 |
Citations | PageRank | References |
46 | 2.83 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nafiz Arica | 1 | 218 | 17.26 |
| Yarman-Vural, F.T. | 2 | 51 | 4.06 |