Writer Identity Recognition and Confirmation Using Persian Handwritten Texts

There are many ways to recognize the identity of individuals and authenticate them and the modern world still is looking for unique biometric features of humans. The recognition and authentication of individuals with the help of their handwriting is regarded as a research topic in recent years. It is widely used in the field of security, legal, access control to systems and financial activities. This thesis tries to examines the identification and authentication of individuals in Persian (Farsi) handwritten texts so that the identity of the author can be determined with a handwritten text, and in the authentication problem, with having two handwritten texts, it is determined that whether both manuscripts belong to a specific person or not. The proposed system for recognizing the identity of the author in this study can be divided into two main parts: one part is intended for training and the other for testing. To assess the performance of introduced characteristics, the Hidden Markov Model (HMM) is used as the classifier; thus, a model is defined for each angular characteristic. The defined angular models are connected by a specific chain network to form a comprehensive database for classification. This database is then used to determine and authenticate the author.

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Development of Speech Recognition Systems in Emergency Call Centers

Symmetry ◽

10.3390/sym13040634 ◽

2021 ◽

Vol 13 (4) ◽

pp. 634

Author(s):

Alakbar Valizada ◽

Natavan Akhundova ◽

Samir Rustamov

Keyword(s):

Speech Recognition ◽

Markov Model ◽

Hidden Markov ◽

Call Centers ◽

The Other ◽

Language Models ◽

Emergency Call ◽

Acoustic Model ◽

Different Types ◽

Recognition Systems

In this paper, various methodologies of acoustic and language models, as well as labeling methods for automatic speech recognition for spoken dialogues in emergency call centers were investigated and comparatively analyzed. Because of the fact that dialogue speech in call centers has specific context and noisy, emotional environments, available speech recognition systems show poor performance. Therefore, in order to accurately recognize dialogue speeches, the main modules of speech recognition systems—language models and acoustic training methodologies—as well as symmetric data labeling approaches have been investigated and analyzed. To find an effective acoustic model for dialogue data, different types of Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) and Deep Neural Network/Hidden Markov Model (DNN/HMM) methodologies were trained and compared. Additionally, effective language models for dialogue systems were defined based on extrinsic and intrinsic methods. Lastly, our suggested data labeling approaches with spelling correction are compared with common labeling methods resulting in outperforming the other methods with a notable percentage. Based on the results of the experiments, we determined that DNN/HMM for an acoustic model, trigram with Kneser–Ney discounting for a language model and using spelling correction before training data for a labeling method are effective configurations for dialogue speech recognition in emergency call centers. It should be noted that this research was conducted with two different types of datasets collected from emergency calls: the Dialogue dataset (27 h), which encapsulates call agents’ speech, and the Summary dataset (53 h), which contains voiced summaries of those dialogues describing emergency cases. Even though the speech taken from the emergency call center is in the Azerbaijani language, which belongs to the Turkic group of languages, our approaches are not tightly connected to specific language features. Hence, it is anticipated that suggested approaches can be applied to the other languages of the same group.

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Handwritten text recognition in Odia script using Hidden Markov Model

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) ◽

10.1109/ncvpripg.2015.7490014 ◽

2015 ◽

Cited By ~ 1

Author(s):

Suman Bhoi ◽

D. P. Dogra ◽

P. P. Roy

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Text Recognition ◽

Handwritten Text ◽

Handwritten Text Recognition

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

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183844 ◽

2018 ◽

pp. 162-180 ◽

Cited By ~ 1

Author(s):

Chit San Lwin ◽

Xiangqian Wu

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Nearest Neighbors ◽

Structural Features ◽

The Other ◽

Self Organization ◽

K Nearest Neighbors ◽

Recognition Process ◽

Recognition Algorithms

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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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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