Handwritten Hindi Character Recognition Using Curvelet Transform

Automatic Character Recognition for the handwritten Indic script has listed up as most the challenging area for research in the field of pattern recognition. Although a great amount of research work has been reported, but all the state-of-art methods are limited with optimal features. This article aims to suggest a well-defined recognition model which harnessed upon handwritten Odia characters and numerals by implementing a novel process of decomposition in terms of 3rd level Fast Discrete Curvelet Transform (FDCT) to get higher dimension feature vector. After that, Kernel-Principal Component Analysis (K-PCA) considered to obtained optimal features from FDCT feature. Finally, the classification is performed by using Probabilistic Neural Network (PNN) on handwritten Odia character and numeral dataset from both NIT Rourkela and IIT Bhubaneswar. The outcome of proposed scheme outperforms better as compared to existing model with optimized Gaussian kernel-based feature set.

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SELECTION TECHNIQUE FOR MULTIPLE OUTPUTS OF OPTICAL CHARACTER RECOGNITION

Eurasian Journal of Mathematical and Computer Applications ◽

10.32523/2306-6172-2020-8-2-41-51 ◽

2020 ◽

Vol 8 (2) ◽

pp. 41-51

Author(s):

I.Q. Habeeb ◽

Z.Q. Al-Zaydi ◽

H.N. Abdulkhudhur

Keyword(s):

Character Recognition ◽

Optical Character Recognition ◽

Selection Technique ◽

Multiple Outputs ◽

Optical Character

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A Structured Method for the Recognition of Complex Historical Tables

History and Computing ◽

10.3366/hac.1997.9.1-3.58 ◽

1997 ◽

Vol 9 (1-3) ◽

pp. 58-77

Author(s):

Vitaly Kliatskine ◽

Eugene Shchepin ◽

Gunnar Thorvaldsen ◽

Konstantin Zingerman ◽

Valery Lazarev

Keyword(s):

Nineteenth Century ◽

Character Recognition ◽

Optical Character Recognition ◽

Complex Structure ◽

Source Material ◽

Historical Sources ◽

Tax Assessment ◽

Optical Character ◽

Algorithmic Model ◽

Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

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