- Open Access
- Total Downloads : 8
- Authors : Kiran Y. C, Lothitha B. J
- Paper ID : IJERTCONV3IS19109
- Volume & Issue : ICESMART – 2015 (Volume 3 – Issue 19)
- Published (First Online): 24-04-2018
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
A Comprehensive Survey on Kannada Handwritten Character Recognition and Dataset Preparation
Kiran Y. C Lothitha B. J
Research Scholar, Jain University M.Tech. Student Dept. of ISE
Associate Professor, Dept. of ISE Dayananda Sagar College of Engineering, VTU Belgaum Dayananda Sagar College of Engineering Bangalore, India
Abstract- Recognition of handwritten text has been one of the active and challenging areas of research in the field of image processing and pattern recognition. Recognition of Kannada handwritten character is complicated compared to other languages. It has numerous applications which include postal mail application, reading aid for blind and conversion of any handwritten document into electronic form. There is no most robust dataset available for handwritten characters. This paper focuses on developing a dataset for offline handwritten Kannada character recognition and overview of the ongoing researches in this field.
Keywords: Optical Character Recognition, Kannada Scripts, Support Vector Machine, Fuzzy K- Nearest Neighbor
Handwriting character recognition has always been a challenging and interesting task in the field of pattern recognition. An Optical Character Recognition (OCR) system is the process of transforming human readable and optically sensed data to machine understandable codes. The purpose behind an OCR is to identify and analyze a document image by dividing the page into line elements, further subdividing into words, and then into characters. Recognition of characters can be done either from printed documents or from hand written documents. The high performance of any recognition system depends on the detailed analysis of preprocessing and segmentation.
Kannada is a Dravidian language spoken primarily in Karnataka State in South India, and has a literature that dates from the ninth century. It is spoken not only in Karnataka, but to some extent in the neighbouring states of Andhra Pradesh, Tamil Nadu, and Maharashtra. Kannada is spoken by about 44 million people. The language has 47 characters in its alphabet set 13 vowels and 34 consonants. The characters called aksharas are formed by graphically combining the symbols corresponding to consonants, consonant modifiers and vowel modifiers using well defined rules of combination. The number of possible consonant-vowel combination is 34*15=544 and number of
possible consonant-consonant-vowel combination is 34*34*16= 18511.
Characters can be in one of the following way
A stand-alone vowel or a consonant
A consonant modified by a vowel
A consonant modified by one or more consonant and a vowel
Almost all South Indian languages are with curve shape. The curve shape and 18496 combinations of Kannada characters are made lot of difficulties for character segmentation as well as for character recognition. Some of the complex characters are listed below to show the complication of the segmentation (Characters of subscripts).
Fig.1.The conjunct consonant (Subscript/Vattu)
DATABASE GENERATION DETAILS
The most robust database for Kannada handwritten characters is not available therefore, our own database is created. Data samples are collected from persons of various ages, occupation and sex. A4 size sheet with boxes are drawn, and writers are requested to write using black gel pen especially concentrating on subscript (Vattu).The only restriction was that the character should not touch the boundary of the boxes on the sheet. We have taken dataset from 25 people. We have given two paper sheets for each person to take data from them. Each paper sheet contains 65 words along with subscript and person has to write each character at two times so that variation can be included in
that. Fig. 2 shows the sample of handwritten data written by a writer.
The A4 size paper sheet having the data written by various writers (Fig. 2) is digitized using Canon Canoscan Lide 100 flatbed scanner at 300 dpi and stored that document in TIFF file format for the better resolution of image and store it in separate folder. Then we have cropped each word from the scanned document and saved cropped image in TIFF file format.
For image cropping we have used Paint Tool. While working with Paint tool we have noticed that it is quite time consuming as each time image need to cropped then need to copy and then paste in new file. Finally need to save in TIFF format.
Fig.2. Scanned Document
Fig.3. Cropped Images
Preprocessing technique is used to do improvement of image data that enhances some image features required for processing and suppresses unwanted noise and distortion from image data and aims to correct degradation in an image.
Binarization: Binarization is the process of converting grayscale image in to binary (Black and White) image, so that image data will only contain 0 and 1.
Noise Removal: Digital image consist of variety of noises. These noises are required to be removed from an image for better processing. Morphological operation, Median filter and Weiner filter is used to remove noise from an image. Median filter reduces blurring of edges.
Thinning and Filling: Smoothing implies both filling and thinning. Thinning reduces width of character while filling eliminates gaps, small breaks and holes in digitized character.
Normalization: To obtain characters of uniform size, rotation and slant normalization is applied on images. It consist fragmentation. Fragmentation it reduce s the un- necessary part of the character where it has two types horizontal and vertical fragmentation.
Skew Detection and Correction: During the digitization of document page it is often that image is not align correctly or it may be happen by human while writing document. To make in correctly align skew detection and correction technique is used. Skew detection technique can be classified in to groups: Analysis of projection profile, Hough transform, clustering, connected component and correlation between line techniques.
Dinesh Acharya U et.al  presented a automatic recognition of handwritten Kannada numerals based on structural features. Profile based 10-segment string, water reservoir; vertical and horizontal strokes, end points and average boundary length from the minimal bounding box are used as the structural features. These multiple features are combined to form a single feature vector. Fuzzy K- nearest neighbor (fuzzy KNN) classification is used in the recognition stage.
Manjunath Aradhya V N et.al  authors proposed unconstrained handwritten Kannada character recognition based on FLD (Fisher Linear Discriminate Analysis).the method extracts features from FLD, 2D-FLD and Diagonal FLD. For classification purpose, authors explored different distance measure techniques and tested there superiority on unconstrained handwritten Kannada characters. The system showed feasibility and effectiveness of method.
Sangame S.K et.al  reported an unconstrained handwritten Kannada vowel recognition based on the invariant moments. The proposed method extracts invariant moments feature from zoned images. KNN classifier and Euclidian distance criterion are the classifiers used to
recognize the handwritten Kannada vowels.1625 data samples are used for the experiment.85.53% accuracy is obtained.
Leena R Ragha et.al  to recognize a kagunita, authors needs to identify the vowel and the consonant dynamically preprocessed original image. Moments and statistical features are extracted from original images, directional images and cut images. These features are used for both vowel and consonant recognition on multi layer perceptron with back propagation neural network. The recognition result for vowels are average 86% and consonants are 65% when tested on separate test data.
Thungamani.M et.al  the objective of this paper is to describe an OCR system for handwritten text documents in Kannada. The system first extracts character and a set of features are extracted from the character image using Zernike moments. The recognition is achieved using Support Vector Machine (SVM). The recognition is independent of the size of the handwritten text and the system is seen to deliver reasonable performance. The recognition rate achieved 94 %.
B.V.Dhandra et.al  in this paper author proposed zone based features is extracted from handwritten Kannada vowels and English uppercase character images for their recognition. A total of 4,000 handwritten Kannada and English sample images are collected for classifications. The normalized images are divided into 64 zones and their pixel densities are calculated. These 64 features are submitted to KNN and SVM classifiers. The recognition accuracy of 92.71% for KNN and 96.00% for SVM classifiers are achieved in case of Kannada vowels and 97.51% for KNN and 98.26% for SVM classifiers are achieved in case English uppercase alphabets.
Suresh Kumar D.S et.al  in this paper, authors recognized handwritten Kannada characters using feed
present in the kagunita character image. In this paper, authors investigate the use of moments features on Kannada kagunita. Kannada characters are curved in nature with some symmetry observed in the shape. So authors are finding 4 directional images using Gabor wavelets from the
forward neural networks. A handwritten Kannada character is resized into 20×30 pixels. 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.
Mamatha H R et.al  in this paper, a segmentation scheme for segmenting handwritten Kannada scripts into lines, words and characters using morphological operations and projection profiles is proposed. The method was tested on totally unconstrained handwritten Kannada scripts. Usage of the morphology made extracting text lines efficiently by an average extraction rate of 94.5%.
In this paper we made a survey on offline handwritten character recognition and dataset collection. We collected data samples from 25 writers of different age, sex, and group. Total of 1000 words are collected. Compared to other languages handwritten Kannada character recognition is very difficult task in that also recognizing the subscripts (Vattu) is very challenging. This survey provides brief information about Kannada handwritten character recognition and dataset preparation.
TABLE 1.Literature survey Summery
It involves elimination of noise in the document
The original image is cut by some percentage from top, right and bottom directions
Moment features from original and directional images
Multi-layer perceptron with back propagation neural network is used
The recognition results for vowels are average 85% and that of consonants are 59%
RGB image is converted to gray scale and then gray scale image is converted to
Three classes, i.e. base characters, modifier Glyphs and subscripts which are recognized separately
K-means clustering is used for extraction
SVM approach is used for classification
The recognition rate is 94.76% in K-means
It involves noise reduction,
size normalization and thinning
The character/numeral image(50Ã—500 is divided into 25 equal zones(10Ã—10
Zone and distance metric based feature extraction system
Feed forward back propagation neural network is used
98% and 96% recognition rate for Kannada and Telugu numerals respectively
Histogram based global binarizing algorithm is used to convert gray image to two-tone images
Bounding box of a character is segmented into blocks
Directional chain code information of the contour points of the characters is used
Quadratic classifier based scheme
97.87% and 98.45%
recognition accuracy using 64 dimensional and 100 dimensional features respectively
Preprocessing stage involves noise reduction, slant correction, size normalization and thinning
Novel character segmentation method using Gabor filters method based on the analyzing the vertical projection of a character is developed to find column index
The features that are used to form a signature are direction of the stroke, density of the stroke and number of clicks for the character
KNN is used for classification
Accuracy is of 94.4%
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