ONLINE HANDWRITING CHARACTER RECOGNITION METHOD USING DIRECTIONAL AND DIRECTION-CHANGE FEATURES

1999 ◽  
Vol 13 (07) ◽  
pp. 1041-1059 ◽  
Author(s):  
MASAYOSHI OKAMOTO ◽  
KAZUHIKO YAMAMOTO
Keyword(s):  
Character Recognition ◽  
Traditional Method ◽  
Recognition Rate ◽  
Recognition Method ◽  
Online Recognition ◽  
Direction Change ◽  
Online Handwriting

We propose a new online recognition method to recognize handwritten cursive-style Japanese characters correctly. Our method simultaneously uses both directional features, otherwise known as offline features, and direction-change features which we designed as online features. The direction-change features express where in the mesh and in which direction the character's coordinates change. These features express both written strokes in the pen-down state and unwritten imaginary strokes in the pen-up state. The recognition rate was improved by our method over the traditional method using only directional features.

scholarly journals Online Handwritten Gurmukhi Character Recognition using Hybrid Feature Set

2018 ◽  
Vol 7 (3.4) ◽  
pp. 90 ◽  
Author(s):  
Mandeep Singh ◽  
Karun Verma ◽  
Bob Gill ◽  
Ramandeep Kaur
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Classification Models ◽  
Stroke Classification ◽  
Online Recognition ◽  
The World ◽  
Gurmukhi Script ◽  
Online Handwriting ◽  
Natural Way

Online handwriting character recognition is gaining attention from the researchers across the world because with the advent of touch based devices, a more natural way of communication is being explored. Stroke based online recognition system is proposed in this paper for a very complex Gurmukhi script. In this effort, recognition for 35 basic characters of Gurmukhi script has been implemented on the dataset of 2019 Gurmukhi samples. For this purpose, 32 stroke classes have been considered. Three types of features have been extracted. Hybrid of these features has been proposed in this paper to train the classification models. For stroke classification, three different classifiers namely, KNN, MLP and SVM are used and compared to evaluate the effectiveness of these models. A very promising “stroke recognition rate” of 94% by KNN, 95.04% by MLP and 95.04% by SVM has been obtained.  

HANDPRINTED CHINESE CHARACTER RECOGNITION USING THE PROBABILITY DISTRIBUTION FEATURE

1994 ◽  
Vol 08 (05) ◽  
pp. 1241-1258 ◽  
Author(s):  
TZE FEN LI ◽  
SHIAW-SHIAN YU
Keyword(s):  
Probability Distribution ◽  
Character Recognition ◽  
Chinese Character ◽  
Recognition Rate ◽  
Bayes Rule ◽  
Chinese Characters ◽  
Two Dimensional ◽  
Shape Variation ◽  
Recognition Method ◽  
Chinese Character Recognition

A simplified Bayes rule is used to classify 5401 categories of handwritten Chinese characters. The main feature for the Bayes rule deals with the probability distribution of black pixels of a thinned character. Our idea is that each Chinese character indicated by the black pixels represents a probability distribution in a two-dimensional plane. Therefore, an unknown pattern is classified into one of 5401 different distributions by the Bayes rule. Since the handwritten character has an irregular shape variation, the whole character is normalized and then thinned. Finally, a transformation is used to spread the black pixels uniformly over the whole square plane, but it still keeps the relative positions of the original black pixels. The main feature gives an 88.65% recognition rate. In order to raise the recognition rate, 4 more subsidiary features are elaborately selected such that they are not affected much by the irregularly shaped variation. The 4 features raise the recognition rate to 93.43%. A 99.30% recognition rate is achieved if the top 10 categories of HCC are selected by our recognition method and 99.61% if the top 20 are selected.

KHMER PRINTED CHARACTER RECOGNITION BY USING WAVELET DESCRIPTORS

2006 ◽  
Vol 14 (03) ◽  
pp. 337-350 ◽  
Author(s):  
C. CHEY ◽  
P. KUMHOM ◽  
K. CHAMNONGTHAI
Keyword(s):  
Character Recognition ◽  
Euclidean Distance ◽  
Recognition Rate ◽  
Wavelet Coefficients ◽  
Training Set ◽  
Recognition Method ◽  
System Testing ◽  
Temporal Domain ◽  
Recognition Result ◽  
Wavelet Descriptor

In Khmer printed characters, same character has various shapes according to the fonts and some characters are very similar in shape. In this paper we try to solve these problems, and propose a method of Khmer printed character recognition by using Wavelet Descriptors. In the recognition, firstly the Khmer printed character images are converted to skeleton forms, then skeletons of Khmer character are converted to temporal domain. The templates are obtained by wavelet coefficients from the character training set. To match the input characters with templates, the character recognition method using deformable wavelet descriptor is adapted by using fixed template and Euclidean distance classifier for matching. The smallest distance is the recognition result of the proposed method. As a result, the deformation can be skipped because it might get low recognition rate of similar characters. The experiment consists of two parts. The first part is to evaluate the overall recognition rate of input characters with three different sizes (22-point, 18-point and 12-point) from 10 different fonts of Khmer printed character. Twenty styles of characters are used as the training set. The results show 92.85, 91.66, and 89.27 percent for 22-point, 18-point, and 12-point respectively. The second part is to specifically evaluate the system, testing with one document that has 21 pages of Khmer printed character with different resolutions from a scanner and facsimile (fax). The document is initially printed with 300 dpi (dots per inch), then scanned with three different resolutions, 600 dpi, 300 dpi and 150 dpi. The document that received from fax machine is scanned by 300 dpi. The results show 92.99, 88.61, and 80.05 percent recognition rate for 300, 150 dpi resolutions, and input from fax respectively.

Printed Thai Character Recognition Using Conditional Random Fields and Hierarchical Centroid Distance

2013 ◽  
Vol 411-414 ◽  
pp. 1238-1246
Author(s):  
Ousanee Sangkathum ◽  
Ohm Sornil
Keyword(s):  
Random Fields ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Conditional Random Fields ◽  
Recognition Rate ◽  
Topological Properties ◽  
Recognition Method ◽  
Model Based ◽  
Centroid Distance

This paper presents a Thai character recognition method based on topological properties. The method first extracts gradient features from a character image. A two-step classification are then applied to recognize the character. In the first step, a conditional random fields model is used to generate a set of possible characters. Then a nearest neighbor model based on hierarchical centroid distance is employed to finally recognize the character. The proposed method is trained by printed characters from documents and vehicle license plates. The technique is evaluated and found to have the recognition rate of 96.96%.

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

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. 

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

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
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.

Machine-Printed Invoice Number Based on Fuzzy Recognition

2012 ◽  
Vol 214 ◽  
pp. 705-710 ◽  
Author(s):  
Xiao Ping Xian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real System ◽  
Recognition Method ◽  
Recognition Time ◽  
Recognition Ability ◽  
System Requirements ◽  
Fuzzy Recognition ◽  
Detection And Separation

A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.

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