Camera Captured based Myanmar Character Recognition Using Dynamic Blocking and Chain Code Normalization

2018 ◽  
Vol 8 (8) ◽  
Author(s):  
Kyi Pyar Zaw ◽  
Zin Mar Kyu
Keyword(s):  
Character Recognition ◽  
Chain Code ◽  
Dynamic Blocking

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.

New Feature Vector from Freeman Chain Code for Handwritten Roman Character Recognition

2018 ◽  
Author(s):  
Dewi Nasien ◽  
Deni Yulianti ◽  
Fakhrul Syakirin Omar ◽  
M. Hasmil Adiya ◽  
Yenny Desnelita ◽  
...  
Keyword(s):  
Character Recognition ◽  
Feature Vector ◽  
Chain Code ◽  
New Feature

Support Vector Machine (SVM) in Handwritten Character Recognition Using Freeman Chain Code (FCC)

2014 ◽  
Vol 20 (10) ◽  
pp. 2171-2175 ◽  
Author(s):  
Dewi Nasien ◽  
Habibollah Haron ◽  
Aini Najwa Azmi ◽  
Siti Sophiayati Yuhaniz
Keyword(s):  
Support Vector Machine ◽  
Character Recognition ◽  
Support Vector ◽  
Handwritten Character Recognition ◽  
Chain Code ◽  
Handwritten Character

AN IMPROVED STRUCTURAL APPROACH FOR AUTOMATED RECOGNITION OF HANDPRINTED CHARACTERS

1991 ◽  
Vol 05 (01n02) ◽  
pp. 97-121 ◽  
Author(s):  
PATRICK SHEN-PEI WANG ◽  
AMAR GUPTA
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Character Recognition ◽  
Line Drawing ◽  
Lower Degree ◽  
Automated Recognition ◽  
Chain Code ◽  
Pattern Recognition Methods ◽  
On Line ◽  
Descriptive Language

This paper examines several line-drawing pattern recognition methods for handwritten character recognition. They are the picture descriptive language (PDL), Berthod and Maroy (BM), extended Freeman's chain code (EFC), error transformation (ET), tree grammar (TG), and array grammar (AG) methods. A new character recognition scheme that uses improved extended octal codes as primitives is introduced. This scheme offers the advantages of handling flexible sizes, orientations, and variations, the need for fewer learning samples, and lower degree of ambiguity. Finally, the simulation of off-line character recognition by the real-time on-line counterpart is investigated.

scholarly journals ALPHABETS IMAGE IDENTIFICATION USING ADVANCED LOCAL BINARY PATTERN AND CHAIN CODE ALGORITHM

2021 ◽  
Vol 2 (2) ◽  
pp. 68
Author(s):  
Daniel Setiawan Cahyono ◽  
Shinta Estri Wahyuningrum
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Local Binary Pattern ◽  
Image Transformation ◽  
Digital Text ◽  
Image Preprocessing ◽  
Chain Code ◽  
Image Identification ◽  
Optical Character ◽  
Code Algorithm

Optical Character Recognition (OCR) is a method for computer to process an image that contains some text and then try to find any characters in that image, then convert it to digital text. In this research, Advanced Local Binary Pattern and Chain Code algorithm will be tested to identify alphabets in the image. Several method image preprocessing are also needed, such as image transformation, image rescaling, grayscale conversion, edge detection and edge thinning.

scholarly journals Pengenalan Karakter Plat Nomor Kendaraan Bermotor Menggunakan Zoning dan Fitur Freeman Chain Code

2018 ◽  
Vol 14 (1) ◽  
pp. 19-25
Author(s):  
Taufik Fuadi Abidin ◽  
Abbas Adam AzZuhri ◽  
Fitri Arnia
Keyword(s):  
Support Vector Machines ◽  
Character Recognition ◽  
Classification Problem ◽  
The Other ◽  
Support Vector ◽  
License Plate ◽  
Chain Code ◽  
Area Code ◽  
Vector Machines ◽  
Multi Class Classification

A license plate is one of the vehicle identities. It consists of alphabetic characters and numbers and represents provincial and area code where the vehicle is registered. This article discusses the character recognition of plate number using zoning and Freeman Chain Code (FCC). Zoning divides character image into several zones i.e. 4, 6, and 8, and then, the pattern of each character in the zone is extracted using FCC as the numerical features. The character is then classified using Support Vector Machines (SVM). It is a multi-class classification problem with 36 categories. The results show that FCC features with 8 zones give the best accuracy (87%) when compared to the other two zones.

Performance of Meta-Heuristic Techniques in Freeman Chain Code (FCC) Extraction for Handwritten Character Recognition

2014 ◽  
Vol 20 (10) ◽  
pp. 2106-2110 ◽  
Author(s):  
Dewi Nasien ◽  
Habibollah Haron ◽  
Siti Sophiyati Yuhaniz ◽  
Aini Najwa Azmi ◽  
Haswadi Hassan
Keyword(s):  
Character Recognition ◽  
Handwritten Character Recognition ◽  
Chain Code ◽  
Handwritten Character ◽  
Heuristic Techniques
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