Design and Realization of On-Line Uyghur Handwritten Character Collection System

2014 ◽  
Vol 989-994 ◽  
pp. 4742-4746
Author(s):  
Halmurat Dilmurat ◽  
Kurban Ubul
Keyword(s):  
Data Collection ◽  
Word Recognition ◽  
Character Recognition ◽  
Recognition System ◽  
Collection System ◽  
System A ◽  
Handwritten Character ◽  
On Line ◽  
Recognition Systems ◽  
Whole Systems

Data collection is the first step in handwritten character recognition systems, and the data quality collected effects the whole systems efficiency. As the necessary subsystem of on-line handwritten character/word recognition system, a Uyghur handwritten character collection system is designed and implemented with Visual C++ based on the nature of Uyghur handwriting. Uyghur handwritings is encoded by 8 direction tendency and stored in extension stroke file. And they are collected based on the content of Text Prompt File. From experimental results, it can be concluded that the handwriting collection system indicates its strong validity and efficiency during the collection of Uyghur handwriting.

A STRUCTURAL APPROACH TO ON-LINE CHARACTER RECOGNITION: SYSTEM DESIGN AND APPLICATIONS

1991 ◽  
Vol 05 (01n02) ◽  
pp. 311-335 ◽  
Author(s):  
FATHALLAH NOUBOUD ◽  
RÉJEAN PLAMONDON
Keyword(s):  
Character Recognition ◽  
Computation Time ◽  
Experimental Tests ◽  
Recognition System ◽  
Structural Approach ◽  
Chain Code ◽  
String Comparison ◽  
Average Computation Time ◽  
On Line ◽  
A Chain

This paper presents a real-time constraint-free handprinted character recognition system based on a structural approach. After the preprocessing operation, a chain code is extracted to represent the character. The classification is based on the use of a processor dedicated to string comparison. The average computation time to recognize a character is about 0.07 seconds. During the learning step, the user can define any set of characters or symbols to be recognized by the system. Thus there are no constraints on the handprinting. The experimental tests show a high degree of accuracy (96%) for writer-dependent applications. Comparisons with other system and methods are discussed. We also present a comparison between the processor used in this system and the Wagner and Fischer algorithm. Finally, we describe some applications of the system.

scholarly journals A Survey on the Classifiers in On-line Handwritten Uyghur Character Recognition System

2016 ◽  
Vol 9 (3) ◽  
pp. 189-198 ◽  
Author(s):  
Wujiahemaiti Simayi ◽  
Mayire Ibrayim ◽  
Dilmurat Tursun ◽  
Askar Hamdulla
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
On Line

Cancellable Biometrics for On-line Signature Recognition

2011 ◽  
pp. 290-315 ◽  
Author(s):  
Emanuele Maiorana ◽  
Patrizio Campisi ◽  
Alessandro Neri
Keyword(s):  
Recognition System ◽  
Design Of Algorithms ◽  
Biometric Data ◽  
The Public ◽  
Protection Scheme ◽  
Protection Method ◽  
Signature Database ◽  
On Line ◽  
Signature Recognition ◽  
Recognition Systems

With the widespread diffusion of biometrics-based recognition systems, there is an increasing awareness of the risks associated with the use of biometric data. Significant efforts are therefore being dedicated to the design of algorithms and architectures able to secure the biometric characteristics, and to guarantee the necessary privacy to their owners. In this work we discuss a protected on-line signature-based biometric recognition system, where the considered biometrics are secured by applying a set of non-invertible transformations, thus generating modified templates from which retrieving the original information is computationally as hard as random guessing it. The advantages of using a protection method based on non-invertible transforms are exploited by presenting three different strategies for the matching of the transformed templates, and by proposing a multi-biometrics approach based on score-level fusion to improve the performances of the considered system. The reported experimental results, evaluated on the public MCYT signature database, show that the achievable recognition rates are only slightly affected by the proposed protection scheme, which is able to guarantee the desired security and renewability for the considered biometrics.

Investigation of ANFIS and FFBNN Recognition Methods Performance in Tamil Speech Word Recognition

2014 ◽  
Vol 2 (2) ◽  
pp. 43-53 ◽  
Author(s):  
S. Rojathai ◽  
M. Venkatesulu
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Inference System ◽  
Feed Forward Back Propagation ◽  
Statistical Measures ◽  
Recognition Systems

In speech word recognition systems, feature extraction and recognition plays a most significant role. More number of feature extraction and recognition methods are available in the existing speech word recognition systems. In most recent Tamil speech word recognition system has given high speech word recognition performance with PAC-ANFIS compared to the earlier Tamil speech word recognition systems. So the investigation of speech word recognition by various recognition methods is needed to prove their performance in the speech word recognition. This paper presents the investigation process with well known Artificial Intelligence method as Feed Forward Back Propagation Neural Network (FFBNN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Tamil speech word recognition system with PAC-FFBNN performance is analyzed in terms of statistical measures and Word Recognition Rate (WRR) and compared with PAC-ANFIS and other existing Tamil speech word recognition systems.

Elastic Matching Techniques for Handwritten Character Recognition

2008 ◽  
pp. 17-38
Author(s):  
Seiichi Uchida
Keyword(s):  
Statistical Analysis ◽  
Character Recognition ◽  
Practical Importance ◽  
Handwritten Character Recognition ◽  
Work Related ◽  
Handwritten Character ◽  
Recognition Systems ◽  
Matching Models ◽  
Future Work ◽  
Matching Techniques

This chapter reviews various elastic matching techniques for handwritten character recognition. Elastic matching is formulated as an optimization problem of planar matching, or pixel-to-pixel correspondence, between two character images under a certain matching model, such as affine and nonlinear. Use of elastic matching instead of rigid matching improves the robustness of recognition systems against geometric deformations in handwritten character images. In addition, the optimized matching represents the deformation of handwritten characters and thus is useful for statistical analysis of the deformation. This chapter argues the general property of elastic matching techniques and their classification by matching models and optimization strategies. It also argues various topics and future work related to elastic matching for emphasizing theoretical and practical importance of elastic matching.

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