scholarly journals Augmentation of Local, Global Feature Analysis for online Character Recognition System for Telugu Language using Feed Forward Neural Networks (FFNN)

2019 ◽  
Vol 8 (9) ◽  
pp. 1677-1683
Keyword(s):  
Character Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Feature Analysis ◽  
Global Feature ◽  
Global Features ◽  
Ann Modeling ◽  
Feed Forward Neural Networks ◽  
F Measure

In this paper, we propose ANN based online handwritten character recognition for Telugu Language. In literature review, it is observed that Size of the database and preprocessing approaches plays prominent role in the recognition performance. Preprocessing techniques like normalization, interpolation, Uniformization, Smoothing, Slant Correction and resampling techniques are performed for better recognition performance. Local features like(x,y)co-ordinates, (x,y) ( , ) 2 2  x  y and the global features like tan() are considered as features for ANN modeling and Classification of 52 Telugu vowels and consonants. Recognition performance is evaluated by augmentation the local, global features and and tan () Features. Theperformance is evaluated in terms of precision, recall and F-measure. Significant Improvement is reported by augmentation andby adopting preprocessing techniques. The database used for the study is HP-online Telugu database.

A Reliability Design Methodology for Chinese Character Recognition

1998 ◽  
Vol 12 (02) ◽  
pp. 159-172 ◽  
Author(s):  
Y. S. Huang ◽  
K. Liu ◽  
C. Y. Suen ◽  
Y. Y. Tang
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition System ◽  
Reliability Design ◽  
Chinese Character Recognition ◽  
Nearest Neighbor Rule ◽  
Pattern Distribution

This paper proposes a novel method which enables a Chinese character recognition system to obtain reliable recognition. In this method, two thresholds, i.e. class region thresholdRk and disambiguity thresholdAk, are used by each Chinese character k when the classifier is designed based on the nearest neighbor rule, where Rk defines the pattern distribution region of character k, and Ak prevents the samples not belonging to character k from being ambiguously recognized as character k. A novel algorithm to derive the appropriate thresholds Ak and Rk is developed so that a better recognition reliability can be obtained through iterative learning. Experiments performed on the ITRI printed Chinese character database have achieved highly reliable recognition performance (such as 0.999 reliability with a 95.14% recognition rate), which shows the feasibility and effectiveness of the proposed method.

scholarly journals KANNADA CHARACTER RECOGNITION SYSTEM USING NEURAL NETWORK

2012 ◽  
pp. 206-208
Author(s):  
SURESH KUMAR D S ◽  
AJAY KUMAR B R ◽  
K SRINIVASA KALYAN
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Indian Language ◽  
Research Areas ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
Hidden Layer

Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form[1]. As there are no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized into 20x30 pixel.The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

scholarly journals Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

2022 ◽  
Vol 12 (2) ◽  
pp. 1849
Author(s):  
Karanrat Thammarak ◽  
Prateep Kongkla ◽  
Yaowarat Sirisathitkul ◽  
Sarun Intakosum
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Color Image ◽  
Recognition Performance ◽  
Recognition System ◽  
Image Characteristics ◽  
Vehicle Registration ◽  
Processing Techniques ◽  
Image Type

Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system.

scholarly journals Classification of Handwritten Tamil Characters using Variable Length Puzzle Pieces

2019 ◽  
Vol 8 (12) ◽  
pp. 4797-4801
Keyword(s):  
Character Recognition ◽  
Feature Vector ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Variable Length ◽  
Indian Languages ◽  
Huge Number ◽  
South Indian ◽  
Tamil Script

offline handwritten character recognition system has been a challenge for Indian scripts, especially for South Indian languages. Huge number of characters of local languages including alphabets, consonants and composite characters make the recognition system more complicated. A good recognition system for subset of Tamil script, a famous South Indian script, is proposed in this work. Variable length feature vector is extracted from the thinned character image. This extracted feature is given to a novel simple classification algorithm which works based on probability. A subset of Tamil script, 20 character classes, is considered for experiment. The samples were taken from HP Labs dataset for Tamil language and a recognition accuracy of 88.15% has been produced.

Ambiguity Reduction through Optimal Set of Region Selection Using GA and BFO for Handwritten Bangla Character Recognition

2015 ◽  
pp. 611-639
Author(s):  
Nibaran Das ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Human Beings ◽  
Bacterial Foraging Optimization ◽  
Global Features ◽  
Data Set ◽  
Region Selection ◽  
Longest Run ◽  
Ambiguity Reduction ◽  
Optimal Set

To recognize different patterns, identification of local regions where the pattern classes differ significantly is an inherent ability of the human cognitive system. This inherent ability of human beings may be imitated in any pattern recognition system by incorporating the ability of locating the regions that contain the maximum discriminating information among the pattern classes. In this chapter, the concept of Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) are discussed to identify those regions having maximum discriminating information. The discussion includes the evaluation of the methods on the sample images of handwritten Bangla digit and Basic character, which is a subset of Bangla character set. Different methods of sub-image or local region creation such as random creation or based on the Center of Gravity (CG) of the foreground pixels are also discussed here. Longest run features, extracted from the generated local regions, are used as local feature in the present chapter. Based on these extracted local features, together with global features, the algorithms are applied to search for the optimal set of local regions. The obtained results are higher than that results obtained without optimization on the same data set.

On Handwriting Recognition System Performance: Some Experimental Results

1992 ◽  
Vol 36 (4) ◽  
pp. 283-287 ◽  
Author(s):  
Paulo J. Santos ◽  
Amy J. Baltzer ◽  
Albert N. Badre ◽  
Richard L. Henneman ◽  
Michael S. Miller
Keyword(s):  
Character Recognition ◽  
Recognition Performance ◽  
Handwriting Recognition ◽  
Extended Period ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Performance Limits ◽  
Novice Users ◽  
Continuous Feedback ◽  
Future Work

Performance of a rule-based handwriting recognition system is considered. Performance limits of such systems are defined by the robustness of the character templates and the ability of the system to segment characters. Published performance figures, however, are typically based on pre-segmented characters. Six experiments are reported (using a total of 128 subjects) that tested a state-of-the-art recognition system under more realistic conditions. Variables investigated include display format (grid, lined, and blank), surface texture, feedback (location and time delay), amount of training, practice, and effects of use over an extended period. Results indicated that novice users writing on a lined display (the most preferred format) averaged 57% recognition performance. By giving subjects continuous feedback of results, training, and after about 10 minutes of use, the system averaged 90.6% character recognition. Following three hours of interrupted use and with performance incentives, subjects achieved an average 96.8% accuracy with the system. Future work should focus on improving the ability of the recognition algorithm to segment characters and on developing non-obtrusive interaction techniques to train users, to provide feedback and to correct mis-recognized characters.

scholarly journals Image Enhancement of Complex Document Images Using Histogram of Gradient Features

2018 ◽  
Vol 7 (4.36) ◽  
pp. 780
Author(s):  
Sajan A. Jain ◽  
N. Shobha Rani ◽  
N. Chandan
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Processing System ◽  
Recognition System ◽  
Document Images ◽  
Uniform Illumination ◽  
Optical Character ◽  
Research Challenge

Enhancement of document images is an interesting research challenge in the process of character recognition. It is quite significant to have a document with uniform illumination gradient to achieve higher recognition accuracies through a document processing system like Optical Character Recognition (OCR). Complex document images are one of the varied image categories that are difficult to process compared to other types of images. It is the quality of document that decides the precision of a character recognition system. Hence transforming the complex document images to a uniform illumination gradient is foreseen. In the proposed research, ancient document images of UMIACS Tobacco 800 database are considered for removal of marginal noise. The proposed technique carries out the block wise interpretation of document contents to remove the marginal noise that is present usually at the borders of images. Further, Hu moment’s features are computed for the detection of marginal noise in every block. An empirical analysis is carried out for classification of blocks into noisy or non-noisy and the outcomes produced by algorithm are satisfactory and feasible for subsequent analysis. 

Ambiguity Reduction Through Optimal Set of Region Selection Using GA and BFO for Handwritten Bangla Character Recognition

2018 ◽  
pp. 1279-1306
Author(s):  
Nibaran Das ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Human Beings ◽  
Bacterial Foraging Optimization ◽  
Global Features ◽  
Data Set ◽  
Region Selection ◽  
Longest Run ◽  
Ambiguity Reduction ◽  
Optimal Set

To recognize different patterns, identification of local regions where the pattern classes differ significantly is an inherent ability of the human cognitive system. This inherent ability of human beings may be imitated in any pattern recognition system by incorporating the ability of locating the regions that contain the maximum discriminating information among the pattern classes. In this chapter, the concept of Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) are discussed to identify those regions having maximum discriminating information. The discussion includes the evaluation of the methods on the sample images of handwritten Bangla digit and Basic character, which is a subset of Bangla character set. Different methods of sub-image or local region creation such as random creation or based on the Center of Gravity (CG) of the foreground pixels are also discussed here. Longest run features, extracted from the generated local regions, are used as local feature in the present chapter. Based on these extracted local features, together with global features, the algorithms are applied to search for the optimal set of local regions. The obtained results are higher than that results obtained without optimization on the same data set.

scholarly journals Automated Degradation Diagnosis in Character Recognition System Subject to Camera Vibration

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Chunmei Liu
Keyword(s):  
Character Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Experimental Results ◽  
System Efficiency ◽  
Syntactic Approach ◽  
Degradation Diagnosis

Degradation diagnosis plays an important role for degraded character processing, which can tell the recognition difficulty of a given degraded character. In this paper, we present a framework for automated degraded character recognition system by statistical syntactic approach using 3D primitive symbol, which is integrated by degradation diagnosis to provide accurate and reliable recognition results. Our contribution is to design the framework to build the character recognition submodels corresponding to degradation subject to camera vibration or out of focus. In each character recognition submodel, statistical syntactic approach using 3D primitive symbol is proposed to improve degraded character recognition performance. In the experiments, we show attractive experimental results, highlighting the system efficiency and recognition performance by statistical syntactic approach using 3D primitive symbol on the degraded character dataset.

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

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