Arabic Handwriting Recognition Competitions

2012 ◽  
pp. 395-422 ◽  
Author(s):  
Volker Märgner ◽  
Haikal El Abed
Keyword(s):  
Handwriting Recognition

Offline Arabic Handwriting Recognition System Based on the Combination of Multiple Semi-Continuous HMMs

2015 ◽  
Vol 10 (7) ◽  
pp. 677
Author(s):  
Ahlam Maqqor ◽  
Akram Halli ◽  
Khalid Satori ◽  
Hamid Tairi
Keyword(s):  
Handwriting Recognition ◽  
Recognition System

User Dependent Handwriting Recognition using off-line OCR for Arithmetic Operation and Text Processing

2011 ◽  
Vol 6 (1) ◽  
pp. 28-35
Author(s):  
D.P. Gaikwad ◽  
Yogesh Gunge ◽  
Raghunandan Mundada ◽  
Himani Bharadwaj ◽  
Swapnil Patil
Keyword(s):  
Text Processing ◽  
Handwriting Recognition ◽  
Arithmetic Operation

Offline Handwriting Recognition to Determine the Handwriting Accents – A Pilot Study

2020 ◽  
Author(s):  
Vinny Sharma ◽  
A.K. Jain ◽  
Prashant Johri ◽  
Gunjan Jindal
Keyword(s):  
Pilot Study ◽  
Handwriting Recognition ◽  
Offline Handwriting Recognition

Approaches to Handwriting Recognition

2020 ◽  
Author(s):  
Dilnoz Muhamediyeva ◽  
Dilshodbek Sotvoldiyev ◽  
Sanjar Mirzaraxmedova ◽  
Madina Fozilova
Keyword(s):  
Handwriting Recognition

HANDWRITTEN WORD RECOGNITION USING CLASSIFIER ENSEMBLES GENERATED FROM MULTIPLE PROTOTYPES

2004 ◽  
Vol 18 (05) ◽  
pp. 957-974 ◽  
Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE
Keyword(s):  
Handwriting Recognition ◽  
Recognition Rate ◽  
Classifier Ensembles ◽  
Multiple Classifier Systems ◽  
Classification Problems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
System 1

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.

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