Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A new scheme for off-line handwrittten connected digit recognition
Date
1998-04-23
Author
Arica, N
Yarman Vural, Fatoş Tunay
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
176
views
0
downloads
Cite This
In this study, we introduce a new scheme for off-line handwritten connected digit string recognition problem, which uses a sequence of segmentation and recognition algorithms. The proposed system assumes no constraint in writing style, size or variations.
Subject Keywords
Hidden Markov models
,
Segmentation
,
Optical character recognition
URI
https://hdl.handle.net/11511/62685
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
A new scheme for off-line handwritten connected digit recognition
Arica, N; Yarman Vural, Fatoş Tunay (1998-08-20)
A new scheme is proposed for off-line handwritten connected digit recognition, which uses a sequence of segmentation and recognition algorithms. First, the connected digits are segmented by employing both the gray scale and binary information. Then, a new set of features is extracted from the segments. The parameters of the feature set are adjusted during the training stage of the Hidden Markov Model (HMM) where the potential digits are recognized. Finally, in order to confirm the preliminary segmentation a...
Optical character recognition for cursive handwriting
Arica, N; Yarman Vural, Fatoş Tunay (2002-06-01)
In this paper, a new analytic scheme, which uses a sequence of segmentation and recognition algorithms, is proposed for offline cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, and stroke width and height are estimated. Second, a segmentation method finds character segmentation paths by combining gray scale and binary information. Third, Hidden Markov Model (HMM) is employed for shape recognition to label and rank the character candidates. For this purp...
A heuristic algorithm for optical character recognition of Arabic script
YarmanVural, FT; Atici, A (1996-03-20)
In this paper, a heuristic method is developed for segmentation, feature extraction and recognition of the Arabic script. The study is part of a large project for the transcription of the documents in Ottoman Archives. A geometrical and topological feature analysis method is developed for segmentation and feature extraction stages. Chain code transformation is applied to main strokes of the characters which are then classified by the hidden Markov model (HMM) in the recognition stage. Experimental results i...
A new electromagnetic target classification method with MUSIC algorithm
Secmen, Mustafa; Sayan, Gönül (2006-01-01)
This paper introduces a novel method for aspect invariant electromagnetic target recognition based on the use of multiple signal classification (MUSIC) algorithm to extract late-time resonant target features from the ultra-wideband scattered data. This method is mainly based on the usage of MUSIC spectra obtained from electromagnetic scattered data as the target features. This approach achieves very high accuracy rates even at very low signal-to-noise ratio (SNR) values although it needs scattered data for ...
Low-level multiscale image segmentation and a benchmark for its evaluation
Akbaş, Emre (Elsevier BV, 2020-10-01)
In this paper, we present a segmentation algorithm to detect low-level structure present in images. The algorithm is designed to partition a given image into regions, corresponding to image structures, regardless of their shapes, sizes, and levels of interior homogeneity. We model a region as a connected set of pixels that is surrounded by ramp edge discontinuities where the magnitude of these discontinuities is large compared to the variation inside the region. Each region is associated with a scale that d...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
N. Arica and F. T. Yarman Vural, “A new scheme for off-line handwrittten connected digit recognition,” 1998, p. 329, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62685.