Gujarati Character Research Articles

CNN-Based Optical Character Recognition for Isolated Printed Gujarati Characters and Handwritten Numerals

Optical character recognition (OCR) technologies have made significant progress in the field of language recognition. Gujarati is a more difficult language to recognize compared to other languages because of curves, close loops, the inclusion of modifiers, and the presence of joint characters. So great effort has been laid into the literature for Gujarati OCR. Recently deep learning-based CNN models are applied to develop OCR for different languages but Convolutional Neural Networks (CNN) models are not yet giving a satisfactory performance to recognize Gujarati characters. So, this paper proposes a revolutionary Gujarati printed characters and numerals recognition CNN models. CNN-PGC (CNN for - Printed Gujarati Character) and CNN-HGC (CNN for - Handwritten Gujarati Character) are two optimally configured Convolutional Neural Networks (CNNs) presented in this research for printed Gujarati base characters and handwritten numbers, respectively. Concerning particular performance indicators, the suggested work's performance is evaluated and proven against that of other traditional models and with the latest baseline methods. Experimental analysis has been carried out on well-segmented newly generated Gujarati base characters and numerals dataset which includes 36 consonants, 13 vowels, and 10 handwritten numerals. Variation in the database is also taken into consideration during experiments like size, skew, noise blue, etc. Even in the presence of printing irregularities, writing irregularities, and degradations the proposed method achieves a 98.08% recognition rate for print characters and a 95.24 % recognition rate for handwritten numerals which is better than other existing models.

Handwritten Steno Character Recognition in Gujarati

Abstract: Steno is symbol of shorthand character which is used to record spoken word. court reporters use the steno language for quick recording of each moment in court room. Some stenographer can write 300 word per minute. many implementations has been done in recognition of different steno symbol into particular language. In this paper we recognize steno symbol (small script) into Indian language which is gujarati. HSCRG stands for Handwritten Steno Character Recognition In Gujarati. HSCRG Algorithm is a process of recognition steno symbol and extract it into equivalent gujarati character based on PCA and Logistic Regression. HSCRG is based on PCA and Logistic Regression accordingly unsupervised and supervised learning. These system mainly has five stages Preprocessing, Feature Selection, classification, Logistic Regression, Extract into gujarati character.

Online Handwritten Gujarati Character Recognition: Two-Layer Classification Approach

Online Handwritten Gujarati Character Recognition: Two-Layer Classification Approach

Gujarati handwritten numeral recognition through fusion of features and machine learning techniques

Languages have played a major role in Indian history and they continue to influence the lives of Indians till date. Plentiful research on optical character recognition (OCR) techniques for Indian languages such as Hindi, Tamil, Bangla, Kannada, Gurumukhi, Malayalam and Marathi has already been carried out. Research efforts on Gujarati character recognition are few and yet to gain momentum. This paper intends to bring Gujarati character recognition in attention. Methods based on artificial neural network (ANN), support vector machine (SVM) and naive Bayes (NB) classifier are exercised for handwritten Gujarati numerals recognition. Experiments are carried out on two large datasets using three different kinds of features and their fusion. Zone-based, projection profiles-based and chain code-based features are employed as individual features. The paper proposes to use a fusion of these features for learning prediction models. Experimental results show significant improvement over state-of-the-art and validate our proposals.

Gujarati handwritten numeral recognition through fusion of features and machine learning techniques

Languages have played a major role in Indian history and they continue to influence the lives of Indians till date. Plentiful research on optical character recognition (OCR) techniques for Indian languages such as Hindi, Tamil, Bangla, Kannada, Gurumukhi, Malayalam and Marathi has already been carried out. Research efforts on Gujarati character recognition are few and yet to gain momentum. This paper intends to bring Gujarati character recognition in attention. Methods based on artificial neural network (ANN), support vector machine (SVM) and naive Bayes (NB) classifier are exercised for handwritten Gujarati numerals recognition. Experiments are carried out on two large datasets using three different kinds of features and their fusion. Zone-based, projection profiles-based and chain code-based features are employed as individual features. The paper proposes to use a fusion of these features for learning prediction models. Experimental results show significant improvement over state-of-the-art and validate our proposals.

Classification of Printed Gujarati Characters Using Low-Level Stroke Features

This article presents an elegant technique for extracting the low-level stroke features, such as endpoints, junction points, line elements, and curve elements, from offline printed text using a template matching approach. The proposed features are used to classify a subset of characters from Gujarati script. The database consists of approximately 16,782 samples of 42 middle-zone symbols from the Gujarati character set collected from three different sources: machine printed books, newspapers, and laser printed documents. The purpose of this division is to add variety in terms of size, font type, style, ink variation, and boundary deformation. The experiments are performed on the database using a k-nearest neighbor (kNN) classifier and results are compared with other widely used structural features, namely Chain Codes (CC), Directional Element Features (DEF), and Histogram of Oriented Gradients (HoG). The results show that the features are quite robust against the variations and give comparable performance with other existing works.

Preprocessing and Segregating Offline Gujarati Handwritten Datasheet for Character Recognition

of a computer to recognize handwritten character is a fascinating area of research due to the peculiarities involved in handwritten characters. Algorithm for Offline handwritten Character recognition differs as a result of diversities involved in writing with various language script. In a task of handwritten character recognition preprocessing and segmentation are two main phases and preliminary steps to be performed on acquired handwritten images. Achieving higher performance in handwritten character recognition depends on feature extraction process, which is highly influenced by preprocessing phase. Proposed work is a first step into an area of offline handwritten Gujarati character recognition. This paper presents algorithm for preprocessing image making it noise free and extracting region of interest for character recognition, segregating datasheet containing 30 characters written in Gujarati script to thirty different images having isolated characters. Further results obtained by employing proposed algorithm is discussed in this paper.

Gujarati character recognition using adaptive neuro fuzzy classifier with fuzzy hedges

Recognition of Indian scripts is a challenging problem and work towards development of an OCR for handwritten Gujarati, an Indian script is still in infancy. This paper implements an Adaptive Neuro Fuzzy Classifier (ANFC) for Gujarati character recognition using fuzzy hedges (FHs). FHs are trained with other network parameters by scaled conjugate gradient training algorithm. The tuned fuzzy hedge values of fuzzy sets improve the flexibility of fuzzy sets; this property of FH improves the distinguishability rates of overlapped classes. This work is further extended for feature selection based on FHs. The values of fuzzy hedges can be used to show the importance of degree of fuzzy sets. According to the FH value, the redundant, noisily features can be eliminated, and significant features can be selected. An FH-based feature selection algorithm is implemented using ANFC. This paper aims to demonstrate recognition of ANFC-FH and improved results of the same with feature selection.