Gujarati Characters Research Articles

ML & DL approach for Handwritten Character Recognition for Gujarati Characters

ML & DL approach for Handwritten Character Recognition for Gujarati Characters

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.

Identification of Typewritten and Handwritten Conjunct Gujarati Characters using Artificial Neural Network

Identification of Typewritten and Handwritten Conjunct Gujarati Characters using Artificial Neural Network

Identification of typewritten and handwritten Conjunct Gujarati characters using artificial neural network

Identification of typewritten and handwritten Conjunct Gujarati characters using artificial neural network

Gujarati Handwritten Character Recognition from Text Images

Today is the era of paperless office and governance. It comes with numerous advantages like increased productivity and efficiency, pervasiveness, storage optimization, robustness and eco-friendliness. Hence there is a need of converting paper documents into machine editable form. This leads to development of OCR (Optical Character Recognition). OCR is a technique to convert, mechanically or electronically an image, photo or scanned document of a handwritten text (HCR-Handwritten Character Recognition) or printed text (PCR- Printed Character Recognition) into digital text. HCR is a form of OCR that is specifically designed to recognize the handwritten text whereas PCR focuses on recognition of printed text. HCR is more challenging as compared to PCR due to diversity in human writing styles, size, curve, strokes and thickness of characters. Based on data acquisition mode, the OCR can either be online or offline. Offline recognition is performed in two ways: handwritten and printed [1]. In offline mode, the characters are on paper and captured using scanner or high-resolution camera whereas in online mode the pixel values of characters are captured by movement of cursor, pen or stylus. The HCR systems are readily available for foreign languages and many of the Indian languages like Bangla, Devanagari and Gurumukhi but for Gujarati language the HCR development is still in its infancy stage. This study focuses on development of an artificial intelligence based offline HCR system for Gujarati language. Important contribution of this study is data collection, of size 10,000 images from 250 number of people, of different age groups, of different professions. This paper describes a supervised classifier approach based on CNN (Convolutional Neural Networks) and MLP (Multi-Layer Perceptron) for recognition of handwritten Gujarati characters. A success rate of 97.21% is obtained using CNN and 64.48% using MLP. Lot of work has been done at character level, but very few has been done at word level recognition. Major focus of this study was on creating a continuous workflow for image to text conversion at word level.

Handwritten Gujarati Character Recognition Using Structural Decomposition Technique

Handwritten character recognition is the active area of research. Development of Optical Character Recognition (OCR) system for Indian script like Gujarati is still in infancy and hence, there exists many unaddressed challenging problems for research community in this domain. The paper proposes three novel features to represent handwritten Gujarati characters. These features include features extracted based on structural decomposition, zone pattern matching and normalized cross correlation. Methods based on Support Vector Machine (SVM) and Naive Bayes (NB) classifiers have been exercised for the classification of Gujarati characters represented using proposed features. Experiments have been carried out on a dataset of 20500 handwritten Gujarati characters. Experimental results showed significant improvement over state-of-the-art when classifiers were learnt using structural decomposition based features.

A Study to Recognize Printed Gujarati Characters Using Tesseract OCR

A Study to Recognize Printed Gujarati Characters Using Tesseract OCR

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.

A Phonetic Study for Constructing a Database of Gujarati Characters for Speech Synthesis of Gujarati Text

Authors are under procedure to develop Gujarati Text to speech(TTS) synthesis system using concatenation of Gujarati phonemes for unrestricted input Gujarati text. In this methodology, Gujarati phonemes are needed to record and store as speech database. This paper describes a flexible procedure to develop speech database in .wav format. This method is simple to implement which uses less memory space.

Structural Feature Extraction to recognize some of the Offline isolated Handwritten Gujarati Characters using Decision Tree Classifier

amount of information is prevailing on paper and in an era of digital technology it requires it to store this information in electronic format. Using scanner this information can be digitized. Later any modification in terms of add, editing, removing and searching to it requires a technique or methodology which will identify text from image and convert into ASCII or Unicode. This paper presents recognition of offline handwritten character for Gujarati script using structural features. For proposed experimental work five characters of Gujarati script are considered. Decision tree classifier is proposed for classification. Various phases of character recognition are implemented such as collecting handwritten input, digitization, preprocessing, extracting structural features and recognition. By proposed work authors are able to achieve 88.78% of average success rate for defined five characters.

Recognition of Similar appearing Gujarati Characters using Fuzzy-KNN Algorithm

s paper describes the Optical Character Recognition of similar appearing characters of Gujarati language. Gujarati language is a type of Indian language. Recognition accuracy of Gujarati Script is affected by characters very similar in shape. Here, Fuzzy KNN classifier in pair with two different features Geometric and Wavelet features are used to handle this problem. Fuzzy KNN not only label the class of pattern to be identified, it also decides strength of that pattern for that class. This makes use of Fuzzy KNN for imprecise class boundary. The test data for similar appearing characters are collected from various sources like scanned pages of text books of Gujarati language, newspapers etc. Train data set is prepared by typing Gujarati characters in different font types and size and then scanned.