Feature extraction methods for character recognition-A survey

doi:10.1016/0031-3203(95)00118-2

Pattern Recognition

Volume 29, Issue 4, April 1996, Pages 641-662

https://doi.org/10.1016/0031-3203(95)00118-2 Get rights and content

Abstract

This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.

References (92)

D.G. Elliman et al.
A review of segmentation and contextual analysis for text recognition
Pattern Recognition
(1990)
V.K. Govindan et al.
Character recognition—a review
Pattern Recognition
(1990)
C.Y. Suen et al.
Building a new generation of handwriting recognition systems
Pattern Recognition Lett.
(April 1993)
J. Mantas
An overview of character recognition methodologies
Pattern Recognition
(1986)
P. Gader et al.
Recognition of handwritten digits using template and model matching
Pattern Recognition
(1991)
F. Kimura et al.
Handwritten numerical recognition based on multiple algorithms
Pattern Recognition
(1991)
F.P. Kuhl et al.
Elliptic Fourier features of a closed contour
Comput. Vis. Graphics Image Process.
(1982)
S.O. Belkasim et al.
Pattern recognition with moment invariants: A comparative study and new results
Pattern Recognition
(December 1991)
Y. Li
Reforming the theory of invariant moments for pattern recognition
Pattern Recognition Lett.
(July 1992)
J. Flusser et al.
Pattern recognition by affine moment invariants
Pattern Recognition
(January 1993)

J. Flusser et al.

Affine moment invariants: A new tool for character recognition

Pattern Recognition Lett.

(April 1994)

A. Khotanzad et al.

Rotation invariant image recognition using features selected via a systematic method

Pattern Recognition

(1990)

J.M. Westall et al.

Vertex directed segmentation of handwritten numerals

Pattern Recognition

(October 1993)

J.D. Tubbs

A note on binary template matching

Pattern Recognition

(1989)

I. Sekita et al.

Feature extraction of handwritten Japanese characters by spline functions for relaxation matching

Pattern Recognition

(1988)

T. Taxt et al.

Recognition of handwritten symbols

Pattern Recognition

(1990)

C.-S. Lin et al.

New forms of shape invariants from elliptic Fourier descriptors

Pattern Recognition

(1987)

L. Wang et al.

Detection of curved and straight segments from gray scale topography

CVGIP: Image Understanding

(November 1993)

S.W. Lee et al.

Nonlinear shape normalization methods for the recognition of large-set handwritten characters

Pattern Recognition

(1994)

H. Yamada et al.

A nonlinear normalization method for handprinted Kanji character recognition-line density equalization

Pattern Recognition

(1990)

T. Wakahara

Toward robust handwritten character recognition

Pattern Recognition Lett.

(April 1993)

T. Pavlidis

A vectorizer and feature extractor for document recognition

Comput. Vis, Graphic Image Process.

(1986)

H.-J. Lee et al.

Recognition of handwritten Chinese characters via short line segments

Pattern Recognition

(1992)

F.-H. Cheng et al.

Recognition of handprinted Chinese characters via stroke relaxation

Pattern Recognition

(1993)

S.R. Ramesh

A generalized character recognition algorithm: a graphical approach

Pattern Recognition

(1989)

A. Kundu et al.

Recognition of handwritten word: first and second order hidden Markov model based approach

Pattern Recognition

(1989)

R.P.W. Duin

Superlearning and neural network magic

Pattern Recognition Lett.

(March 1994)

T. Pavlidis

Recognitoin of printed text under realistic conditions

Pattern Recognition Lett.

(April 1993)

S. Mori et al.

Historical review of OCR research and development

J.W. Gorman et al.

Partial shape recognition using dynamic programming

IEEE Trans. Pattern Anal. Mach. Intell.

(March 1988)

E. Persoon et al.

Shape discrimination using Fourier descriptors

IEEE Trans. Syst. Man Cybernet.

(March 1977)

L. Shen et al.

Application of shape analysis to mammographic calcifications

IEEE Trans. Med. Imaging

(June 1994)

C.E. Dunn et al.

Character segmentation techniques for handwritten text—a survey

L. Lam et al.

Thinning methodologies—a comprehensive survey

IEEE Trans. Pattern Anal. Mach. Intell.

(September 1992)

K.-S. Fu

Syntactic Pattern Recognition and Application

(1982)

R.O. Duda et al.

Pattern Classification and Scene Analysis

(1973)

K. Fukunaga

Introduction to Statistical Pattern Recognition

(1990)

P.A. Devijver et al.

Pattern Recognition: A Statistical Approach

(1982)

J.R. Ullmann

Pattern Recognition Techniques

(1973)

Ø.D. Trier et al.

Evaluation of binarization methods for document images

IEEE Trans. Pattern Anal. Mach. Intell.

(March 1995)

Ø.D. Trier et al.

Goal-directed evaluation of binarization methods

IEEE Trans. Pattern Anal. Mach. Intell.

(December 1995)

S.-W. Lee et al.

Performance evaluation of skeletonizing algorithms for document image processing

M.Y. Jaisimha et al.

Quantitative performance evaluation of thinning algorithms under noisy conditions

G. Nagy

At the frontiers of OCR

C.Y. Suen et al.

Computer recognition of unconstrained handwritten numerals

C.Y. Suen et al.

Automatic recognition of handprinted characters—the state of the art

Cited by (941)

Style-independent radical sequence learning for zero-shot recognition of Small Seal script
2023, Journal of the Franklin Institute
Small Seal script (Chinese called XiaoZhuan), as the earliest archaic form of standardized Chinese script, is the predecessor of modern Chinese characters. However, the Small Seal character recognition remains a challenging task, due to various un-/semi-structured pictographic glyphs and writing styles. This paper proposes a style-independent pictographic radical decomposition for the zero-shot recognition of Small Seal script, by taking advantage of the inherent consistency of pictographic representations between the Small Seal script and the Traditional Chinese script (Chinese called Fanti). Specifically, we design a feature-level collaboration framework of two tasks. One is the XiaoZhuan-to-Fanti translation task, which employs a generative adversarial network (GAN) based dual-learning mechanism to learn style-independent and consistent pictographic feature representations from different styles of Small Seal and their corresponding Traditional Chinese characters. The other is a transformer-based pictographic radical sequence learning from the pictographic feature representations. Experiments demonstrate that our model has satisfactory recognition ability to various styles of Small Seal scripts, especially for the zero-shot recognition of those with unknown glyphs and unseen styles. The code is available at https://github.com/windyz77/SmallSealRecon.
Social media user behavior analysis applied to the fashion and apparel industry in the big data era
2023, Journal of Retailing and Consumer Services
With the rapid development of the Internet and new media, the enormous data from social media and other public platforms have attracted increasing attention to user behavior research. Fashion is a hot topic for both the general public and the social economy, so there is great potential for exploring and manipulating social media data for the development of the fashion industry in the current era. This paper aims to explore the current status of social media user behavior analysis applied to the fashion and apparel industry for advanced product development, branding strategy planning and the resolution of other decision-making problems in the big data context. First, articles were retrieved from the database "Web of Science". With the assistance of experts, 201 articles were determined for further study. Next, the evolution of hot topics within the domain was visualized and discussed through bibliometric analysis. Then, the application of social media data in the fashion domain was investigated, and common data mining methods were discussed. Finally, the paper summarized the development status of social media user behavior analysis (SMUBA) applied to the fashion and apparel industry and put forward the future prospect under the framework of mass customization in the big data era.
Threshold center-symmetric local binary convolutional neural networks for bilingual handwritten digit recognition
2023, Knowledge-Based Systems
The writing style of the same writer varies from instance to instance in Arabic and English handwritten digit recognition, making handwritten digit recognition challenging. Currently, deep learning approaches are applied in many applications, including convolutional neural networks (CNNs) modified to produce other models, such as local binary convolutional neural networks (LBCNNs). An LBCNN is created by fusing a local binary pattern (LBP) with a CNN by reformulating the LBP as a convolution layer called a local binary convolution (LBC). However, LBCNNs suffer from the random assignment of 1, 0, or -1 to LBC weights, making LBCNNs less robust. Nevertheless, using another LBP-based technique, such as center-symmetric local binary patterns (CS-LBPs), can address such issues. In this paper, a new model based on CS-LBPs is proposed called center-symmetric local binary convolutional neural networks (CS-LBCNN), which addresses the issues of LBCNNs. Furthermore, an enhanced version of CS-LBCNNs called threshold center-symmetric local binary convolutional neural networks (TCS-LBCNNs) is proposed, which addresses another issue related to the zero-thresholding function. Finally, the proposed models are compared to state-of-the-art models, proving their ability by producing a more accurate and significant classification rate than the existing LBCNN models. For the bilingual dataset, the TCS-LBCNN enhances the accuracy of the LBCNN and CS-LBCNN by 0.15% and 0.03%, respectively. In addition, the comparison shows that the accuracy acquired by the TCS-LBCNN is the second-highest using the MNIST and MADBase datasets.
Segmentation-based, omnifont printed Arabic character recognition without font identification
2022, Journal of King Saud University - Computer and Information Sciences
Optical Character Recognition OCR is an essential part of many real-world applications such as digital archiving, automatic number plate recognition, handle cheques, etc. However, developing an OCR for printed Arabic text is still a challenging and open research field due to the special characteristics of Arabic cursive script. In this paper, we propose a segmentation-based, omnifont, open-vocabulary OCR for printed Arabic text. The proposed approach doesn’t require an explicit font type recognition stage. It uses an explicit, indirect character segmentation method. The presented segmentation method is baseline dependent and employs a hybrid, three-steps character segmentation algorithm to handle the problem of character overlapping. Besides, it uses a set of topological features that are designed and generalized to make the segmentation approach font independent. The segmented characters are fed as an input to a convolutional neural network for feature extraction and recognition. The APTID-MF data set has been used for testing and evaluation. The average accuracy of the proposed segmentation stage is 95%, while the average accuracy of the recognition stage is 99.97%. The whole approach achieves an average accuracy of 95% without using font-type recognition or any post-processing techniques.
A bibliometric analysis of off-line handwritten document analysis literature (1990–2020)
2022, Pattern Recognition
Providing computers with the ability to process handwriting is both important and challenging, since many difficulties (e.g., different writing styles, alphabets, languages, etc.) need to be overcome for addressing a variety of problems (text recognition, signature verification, writer identification, word spotting, etc.). This paper reviews the growing literature on off-line handwritten document analysis over the last thirty years. A sample of 5389 articles is examined using bibliometric techniques. Using bibliometric techniques, this paper identifies (i) the most influential articles in the area, (ii) the most productive authors and their collaboration networks, (iii) the countries and institutions that have led research on the topic, (iv) the journals and conferences that have published most papers, and (v) the most relevant research topics (and their related tasks and methodologies) and their evolution over the years.
Supervised Clustering of Persian Handwritten Images Using Regularization and Dimension Reduction Methods
2024, ACM Transactions on Knowledge Discovery from Data

View all citing articles on Scopus

View full text

Feature extraction methods for character recognition-A survey

Abstract

Pattern Recognition

Pattern Recognition

Pattern Recognition Lett.

Pattern Recognition

Pattern Recognition

Pattern Recognition

Comput. Vis. Graphics Image Process.

Pattern Recognition

Pattern Recognition Lett.

Pattern Recognition

Pattern Recognition Lett.

Pattern Recognition

Pattern Recognition

Pattern Recognition

Pattern Recognition

Pattern Recognition

Pattern Recognition

CVGIP: Image Understanding

Pattern Recognition

Pattern Recognition

Pattern Recognition Lett.

Comput. Vis, Graphic Image Process.

Pattern Recognition

Pattern Recognition

Pattern Recognition

Pattern Recognition

Pattern Recognition Lett.

Pattern Recognition Lett.

Historical review of OCR research and development

Partial shape recognition using dynamic programming

IEEE Trans. Pattern Anal. Mach. Intell.

Shape discrimination using Fourier descriptors

IEEE Trans. Syst. Man Cybernet.

Application of shape analysis to mammographic calcifications

IEEE Trans. Med. Imaging

Character segmentation techniques for handwritten text—a survey

Thinning methodologies—a comprehensive survey

IEEE Trans. Pattern Anal. Mach. Intell.

Syntactic Pattern Recognition and Application

Pattern Classification and Scene Analysis

Introduction to Statistical Pattern Recognition

Pattern Recognition: A Statistical Approach

Pattern Recognition Techniques

Evaluation of binarization methods for document images

IEEE Trans. Pattern Anal. Mach. Intell.

Goal-directed evaluation of binarization methods

IEEE Trans. Pattern Anal. Mach. Intell.

Performance evaluation of skeletonizing algorithms for document image processing

Quantitative performance evaluation of thinning algorithms under noisy conditions

At the frontiers of OCR

Computer recognition of unconstrained handwritten numerals

Automatic recognition of handprinted characters—the state of the art