Optical character recognition of handwritten Arabic using hidden Markov models

2011 ◽  
Author(s):  
Mohannad M. Aulama ◽  
Asem M. Natsheh ◽  
Gheith A. Abandah ◽  
Mohammed M. Olama
Keyword(s):  
Hidden Markov Models ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Optical Character ◽  
Handwritten Arabic

scholarly journals An Efficient Algorithm for Estimating State Sequences in Imprecise Hidden Markov Models

2014 ◽  
Vol 50 ◽  
pp. 189-233 ◽  
Author(s):  
J. De Bock ◽  
G. De Cooman
Keyword(s):  
Hidden Markov Models ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Exact Algorithm ◽  
Mass Function ◽  
The State ◽  
Model Parameters ◽  
State Sequence

We present an efficient exact algorithm for estimating state sequences from outputs or observations in imprecise hidden Markov models (iHMMs). The uncertainty linking one state to the next, and that linking a state to its output, is represented by a set of probability mass functions instead of a single such mass function. We consider as best estimates for state sequences the maximal sequences for the posterior joint state model conditioned on the observed output sequence, associated with a gain function that is the indicator of the state sequence. This corresponds to and generalises finding the state sequence with the highest posterior probability in (precise-probabilistic) HMMs, thereby making our algorithm a generalisation of the one by Viterbi. We argue that the computational complexity of our algorithm is at worst quadratic in the length of the iHMM, cubic in the number of states, and essentially linear in the number of maximal state sequences. An important feature of our imprecise approach is that there may be more than one maximal sequence, typically in those instances where its precise-probabilistic counterpart is sensitive to the choice of prior. For binary iHMMs, we investigate experimentally how the number of maximal state sequences depends on the model parameters. We also present an application in optical character recognition, demonstrating that our algorithm can be usefully applied to robustify the inferences made by its precise-probabilistic counterpart.

HIDDEN MARKOV MODELS IN TEXT RECOGNITION

1995 ◽  
Vol 09 (06) ◽  
pp. 925-958 ◽  
Author(s):  
J.C. ANIGBOGU ◽  
A. BELAÏD
Keyword(s):  
Hidden Markov Models ◽  
Character Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Majority Vote ◽  
The Other ◽  
Distance Measures ◽  
Original Form ◽  
Verification Methods ◽  
Font Identification

A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for “advice” before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually. The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.

scholarly journals A Survey on Arabic Handwritten Script Recognition Systems

2021 ◽  
Vol 11 (2) ◽  
pp. 1-17
Author(s):  
Soumia Djaghbellou ◽  
Abderraouf Bouziane ◽  
Abdelouahab Attia ◽  
Zahid Akhtar
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Research Field ◽  
Research Directions ◽  
Optical Character ◽  
On Line ◽  
Recognition Systems ◽  
Active Research ◽  
Handwritten Arabic ◽  
Open Issues

The optical character recognition (OCR) system is still an active research field in pattern recognition. Such systems can identify, recognize and distinguish electronically between characters and texts, printed or handwritten. They can also do a transformation of such data type into machine-processable form to facilitate the interaction between user and machine in various applications. In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. This paper also presents a survey of the works that have been carried out over recent years. Finally, some open issues and potential research directions have been highlighted

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