2014
DOI: 10.1007/978-3-319-12883-2_26
|View full text |Cite
|
Sign up to set email alerts
|

A Neural Approach to Cursive Handwritten Character Recognition Using Features Extracted from Binarization Technique

Abstract: The feature extraction is one of the most crucial steps for an Optical Character Recognition (OCR) System. The efficiency and accuracy of the OCR System, in recognizing the off-line printed characters, mainly depends on the selection of feature extraction technique and the classification algorithm employed. This chapter focuses on the recognition of handwritten characters of Roman Script by using features which are obtained by using binarization technique. The goal of binarization is to minimize the unwanted i… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
2
0
1

Year Published

2015
2015
2024
2024

Publication Types

Select...
5
3

Relationship

1
7

Authors

Journals

citations
Cited by 12 publications
(3 citation statements)
references
References 38 publications
0
2
0
1
Order By: Relevance
“…Many new techniques have been introduced in research papers to classify handwritten characters and numerals or digits. Shallow networks have already shown promising results for handwriting recognition [19][20][21][22][23][24][25][26]. Hinton et al investigated deep belief networks (DBN), which have three layers along with a grasping algorithm, and recorded an accuracy of 98.75% for the MNIST dataset [27].…”
Section: Review Of Literature and Related Workmentioning
confidence: 99%
“…Many new techniques have been introduced in research papers to classify handwritten characters and numerals or digits. Shallow networks have already shown promising results for handwriting recognition [19][20][21][22][23][24][25][26]. Hinton et al investigated deep belief networks (DBN), which have three layers along with a grasping algorithm, and recorded an accuracy of 98.75% for the MNIST dataset [27].…”
Section: Review Of Literature and Related Workmentioning
confidence: 99%
“…Handwriting recognition has already achieved impressive results using shallow networks [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Many papers have been published with research detailing new techniques for the classification of handwritten numerals, characters and words.…”
Section: Related Workmentioning
confidence: 99%
“…Estos problemas pueden ser resueltos mediante la aplicación de RNAs que, gracias a sus características, permiten resolver problemas de reconocimiento con tolerancia a ruido e invariancia a escalas y rotaciones que pueden ser traducidos a formas y estilos [2]. Estas ventajas han hecho al OCR una de las aplicaciones más comunes en elárea de RNAs y ha sido utilizado en losúltimos años como el ejemplo práctico por excelencia en el campo académico (por ejemplo los trabajos presentados en [3], [4], [5] y [8]) .…”
Section: Introductionunclassified