Optical character recognition for cursive handwriting

Date

2002-06-01

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

181
views

0
downloads

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 purpose, a string of codes is extracted from each segment to represent the character candidates. The estimation of feature space parameters is embedded in HMM training stage together with the estimation of the HMM model parameters. Finally, the lexicon information and HMM ranks are combined in a graph optimization problem for word-level recognition. This method corrects most of the errors produced by segmentation and HMM ranking stages by maximizing an information measure in an efficient graph search algorithm. The experiments in dicate higher recognition rates compared to the available methods reported in the literature.

Subject Keywords

Handwritten word recognition, Preprocessing, Segmentation, Optical character recognition, Cursive handwriting, Hidden Markov model, Search, Graph, Lexicon matching

URI

https://hdl.handle.net/11511/62516

Journal

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

DOI

https://doi.org/10.1109/tpami.2002.1008386

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

A new scheme for off-line handwrittten connected digit recognition Arica, N; Yarman Vural, Fatoş Tunay (1998-04-23) 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.
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...
Continuous dimensionality characterization of image structures Felsberg, Michael; Kalkan, Sinan; Kruger, Norbert (Elsevier BV, 2009-05-04) Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patche...
Information permeability for stereo matching Cigla, Cevahir; Alatan, Abdullah Aydın (Elsevier BV, 2013-10-01) A novel local stereo matching algorithm is introduced to address the fundamental challenge of stereo algorithms, accuracy and computational complexity dilemma. The time consuming intensity dependent aggregation procedure of local methods is improved in terms of both speed and precision. Providing connected 2D support regions, the proposed approach exploits a new paradigm, namely separable successive weighted summation (SWS) among horizontal and vertical directions enabling constant operational complexity. T...
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...

Citation Formats

N. Arica and F. T. Yarman Vural, “Optical character recognition for cursive handwriting,” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, pp. 801–813, 2002, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62516.