Zone-Wise Segmentation and Lexicon-Driven Recognition for Printed Myanmar Characters

This paper presents a new segmentation and recognition algorithms for Myanmar script inputted from offline printed images. Zone segmentation considers horizontal and vertical zones; it is applied to segment letters according to their roles such as primary or peripheral characters. In doing so, statistical and structural features of segmented characters are explored and exploited in recognition process. Hidden Markov model is used for recognition of primary characters while Kohonen self-organization map is used for peripheral characters. The recognized characters by each model are then combined, and finally are recognized by k-nearest neighbors algorithm with the help of lexicon is composed of all common Myanmar characters. Our OCR system for Myanmar characters tested on a dataset that approximately contains 7560 compounded characters. From the results, our system achieves higher significant results both segmentation and recognition compared to the other contemporary Myanmar OCR’s approaches.

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Experiments With Fast Fourier Transform, Linear Predictive and Cepstral Coefficients in Dysarthric Speech Recognition Algorithms Using Hidden Markov Model

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2005.856074 ◽

2005 ◽

Vol 13 (4) ◽

pp. 558-561 ◽

Cited By ~ 23

Author(s):

P.D. Polur ◽

G.E. Miller

Keyword(s):

Fourier Transform ◽

Speech Recognition ◽

Markov Model ◽

Hidden Markov Model ◽

Fast Fourier Transform ◽

Hidden Markov ◽

Recognition Algorithms ◽

Dysarthric Speech ◽

Cepstral Coefficients

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Hidden Markov Model Based Odia Numeral Recognition Using Moment and Structural Features

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8614.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 2317-2325 ◽

Cited By ~ 1

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Character Recognition ◽

Optical Character Recognition ◽

Hidden Markov ◽

Noise Removal ◽

Structural Features ◽

Hard Copy ◽

Complex Nature ◽

Model Based

Optical character recognition (OCR) is a strategy to perceive character from optically checked and digitized pages. OCR plays an important role for Indian script research. The official language of the state Odisha is Odia. OCR face an incredible difficulties to recognize Odia language due to similar shape characters, their complex nature, the complicated way in which they combine form to compound character, use of Matra etc. Each character and numbers are passed through several modules like binarization, noise removal, segmentation, line segmentation, word segmentation, skeletonization, deskewing, thinning, thickening. The input picture is standardized to a size of 50 x 50 2D pictures. HMM is a stochastic process which has utilized in various applications for example speech recognition, Handwriting recognition, Gesture recognition. In this paper we utilized HMM to recognize the Odia character and numbers. Hidden Markov Model have many advantages such as resistant to noise, handle contrast recorded as a hard copy and the HMM devices are effectively accessible. In our proposed method we have developed an efficient recognition algorithm using Hidden Markov model based on moment based and structural feature to recognize Odia characters and numerals.

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Metamorphic malware detection using structural features and nonnegative matrix factorization with hidden markov model

Journal of Computer Virology and Hacking Techniques ◽

10.1007/s11416-021-00404-z ◽

2021 ◽

Author(s):

Yeong Tyng Ling ◽

Nor Fazlida Mohd Sani ◽

Mohd Taufik Abdullah ◽

Nor Asilah Wati Abdul Hamid

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Hidden Markov ◽

Malware Detection ◽

Nonnegative Matrix ◽

Structural Features ◽

Metamorphic Malware

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Implementing Hidden Markov Model to Predict Foreign Exchange Movement

INTEGER: Journal of Information Technology ◽

10.31284/j.integer.2018.v3i1.140 ◽

2018 ◽

Vol 3 (1) ◽

Author(s):

Tri Swasono Himawan ◽

Tutuk Indriyani ◽

Weny Mistarika Rahmawati

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Foreign Exchange ◽

Error Score ◽

Hidden Markov ◽

The Other ◽

Real Sector ◽

Forward Algorithm ◽

Average Deviation

Investment refers to personal bussiness. So many people have got profit from investment both real and non real sectors. Foreign Exchange (FOREX) is the example of non real sector. The currency fluctuation of FOREX usually occurs and this causes many investors fooled by the pattern of currency fluctuation. Finally, they get lost and even lost capital. Hidden Markov Model was implemented in this research to predict the movement of FOREX of 8 currencies. The data were trained by Baum-Welch algorithm and predicted by Forward algorithm. The trial obtained the average MAPE (Mean Absolute Precentage Error) of 8 currencies which was relatively small (0.0038082% belongs to high and 0.0040706% belongs to low), less than 1%. The currency of USD/IDR has the smallest error score among the other tested currencies. Its average MAPE was 0.0032624% and the average deviation was 42. Thus, this system is well proven to predict the movement of currency.

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