A SYNTACTIC OMNI-FONT CHARACTER RECOGNITION SYSTEM

This paper introduces a syntactic omni-font character recognition system. The "omni-font" attribute reflects the wide range of fonts that fall within the class of characters that can be recognized. This includes hand-printed characters as well. A structural pattern-matching approach is employed. Essentially, a set of loosely constrained rules specify pattern components and their interrelationships. The robustness of the system is derived from the orthogonal set of pattern descriptors, location functions, and the manner in which they are combined to exploit the topological structure of characters. By virtue of the new pattern description language, PDL, developed in this paper, the user may easily write rules to define new patterns for the system to recognize. The system also features scale-invariance and user-definable sensitivity to tilt orientation.

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A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i6.287 ◽

2018 ◽

Vol 6 (6) ◽

pp. 307

Author(s):

Manish M. Kayasth ◽

Bharat C. Patel

Keyword(s):

Feature Extraction ◽

Character Recognition ◽

Recognition Rate ◽

Recognition System ◽

Post Processing ◽

Classification Technique ◽

Scanned Image ◽

Gujarati Language ◽

High Degree ◽

Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition.

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Offline Ancient Tamil Character Recognition System Based On Structural Features

i-manager s Journal on Communication Engineering and Systems ◽

10.26634/jcs.1.3.1891 ◽

2012 ◽

Vol 1 (3) ◽

pp. 17-24

Author(s):

S. Rajakumar ◽

V. Subbiah Bharathi

Keyword(s):

Character Recognition ◽

Recognition System ◽

Structural Features

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Handwritten Balinesse Character Recognition using K-Nearest Neighbor

10.31227/osf.io/z6m8u ◽

2018 ◽

Author(s):

I Wayan Agus Surya Darma

Keyword(s):

Feature Extraction ◽

Success Rate ◽

Character Recognition ◽

Nearest Neighbor ◽

Recognition System ◽

Extraction Process ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm ◽

Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

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A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/3/3 ◽

2020 ◽

Vol 17 (3) ◽

pp. 299-305 ◽

Cited By ~ 1

Author(s):

Riaz Ahmad ◽

Saeeda Naz ◽

Muhammad Afzal ◽

Sheikh Rashid ◽

Marcus Liwicki ◽

...

Keyword(s):

Deep Learning ◽

Character Recognition ◽

Data Augmentation ◽

Short Term Memory ◽

Recognition System ◽

Learning Approach ◽

Arabic Text ◽

Data Set ◽

Processing Step ◽

Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

Recent Patents on Engineering ◽

10.2174/1872212112666180601085544 ◽

2019 ◽

Vol 13 (2) ◽

pp. 136-141 ◽

Cited By ~ 2

Author(s):

Abhisek Sethy ◽

Prashanta Kumar Patra ◽

Deepak Ranjan Nayak

Keyword(s):

Feature Extraction ◽

Random Forest ◽

Character Recognition ◽

Recognition Rate ◽

Discrete Wavelet ◽

Gray Level ◽

Handwritten Character Recognition ◽

Handwritten Character ◽

Wide Range ◽

Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

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