One-dimensional representation of two-dimensional information for HMM based handwriting recognition

Date

2000-06-01

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

78
views

0
downloads

In this study, we introduce a one-dimensional feature set, which embeds two-dimensional information into an observation sequence of one-dimensional string, selected from a code-book. It provides a consistent normalization among distinct classes of shapes, which is very convenient for Hidden Markov Model (HMM) based shape recognition schemes. The normalization parameters, which maximize the recognition rate, are dynamically estimated in the training stage of HMM. The proposed recognition system is tested on handwritten data of the National Institute of Standards and Technology (NIST) database and a local database. The experimental results indicate very high recognition rates.

Subject Keywords

Signal Processing, Software, Artificial Intelligence, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/62489

Journal

PATTERN RECOGNITION LETTERS

DOI

https://doi.org/10.1016/s0167-8655(00)00023-4

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Camera auto-calibration using a sequence of 2D images with small rotations Hassanpour, R; Atalay, Mehmet Volkan (Elsevier BV, 2004-07-02) In this study, we describe an auto-calibration algorithm with fixed but unknown camera parameters. We have modified Triggs' algorithm to incorporate known aspect ratio and skew values to make it applicable for small rotation around a single axis. The algorithm despite being a quadratic one is easy to solve. We have applied the algorithm to some artificial objects with known size and dimensions for evaluation purposes. In addition, the accuracy of the algorithm has been verified using synthetic data. The des...
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...
A statistical approach to sparse multi-scale phase-based stereo Ulusoy, İlkay (Elsevier BV, 2007-09-01) In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching uncertain phase are proposed. The features used are oriented edges extracted using steerable filters. Feature correspondences are estimated using phase-similarity at multiple scale using a magnitude weighting scheme. In order to achieve sub-pixel accuracy in disparity, we use a fine tuning procedure which employs the phase difference between corresponding feature points. We also derive a probabilistic ...
Relative consistency of projective reconstructions obtained from the same image pair Otlu, Burcak; Atalay, Mustafa Ümit; Hassanpour, Reza (World Scientific Pub Co Pte Lt, 2006-08-01) This study obtains projective reconstructions of an object or a scene from its image pair and measures relative consistency of these projective reconstructions. 3D points are estimated from an image pair using projective and epipolar geometry. Two measures are presented for verification of projective reconstructions with each other. These measures are based on the equality of ratios between the x-, y- and z-coordinates of 3D reconstructed points which are obtained from the same corresponding points. This in...
Making linear prediction perform like maximum likelihood in Gaussian autoregressive model parameter estimation Candan, Çağatay (Elsevier BV, 2020-01-01) A two-stage method for the parameter estimation of Gaussian autoregressive models is proposed. The proposed first stage is an improved version of the conventional forward-backward prediction method and can be interpreted as its weighted version with the weights derived from the arithmetic mean of the log-likelihood functions for different conditioning cases. The weighted version is observed to perform better than the conventional forward-backward prediction method and other linear prediction based methods (...

Citation Formats

N. Arica and F. T. Yarman Vural, “One-dimensional representation of two-dimensional information for HMM based handwriting recognition,” PATTERN RECOGNITION LETTERS, pp. 583–592, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62489.