Robust recognition technique for handwritten Kannada character recognition using capsule networks

Automated reading of handwritten Kannada documents is highly challenging due to the presence of vowels, consonants and its modifiers. The variable nature of handwriting styles aggravates the complexity of machine based reading of handwritten vowels and consonants. In this paper, our investigation is inclined towards design of a deep convolution network with capsule and routing layers to efficiently recognize  Kannada handwritten characters.  Capsule network architecture is built of an input layer,  two convolution layers, primary capsule, routing capsule layers followed by tri-level dense convolution layer and an output layer.  For experimentation, datasets are collected from more than 100 users for creation of training data samples of about 7769 comprising of 49 classes. Test samples of all the 49 classes are again collected separately from 3 to 5 users creating a total of 245 samples for novel patterns. It is inferred from performance evaluation; a loss of 0.66% is obtained in the classification process and for 43 classes precision of 100% is achieved with an accuracy of 99%. An average accuracy of 95% is achieved for all remaining 6 classes with an average precision of 89%

Recognition of Printed and Handwritten Kannada Characters using SVM Classifier

The optical character recognition is the process of converting textual scanned image into a computer editable format but one of the major challenges faced is the recognition of character from the image. The proposed system is application software for Recognition of Kannada Printed and Handwritten Characters from an image. The input image is subjected for pre-processing to make the image noise free by using median filter and then it is converted to binary image. Segmentation process is carried out to extract one character from the image by performing horizontal segmentation followed by vertical segmentation. Co-relation coefficient is used for extracting the features from the image then the character is classified using SVM classifier finally the classified character is post-processed using its Unicode values to display the recognized character. We have obtained perfectness of 100% and 99% in recognition of Kannada Printed and Handwritten characters respectively

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were looked for up to the year 2023. As a consequence of the inclusion criteria that were developed to concentrate on studies only addressing Handwritten OCR on Indic scripts, 53 research publications were chosen as the process's outcome. The review provides a thorough analysis of the methodology and approaches employed in the chosen study. Deep neural networks, conventional feature-based methods, machine learning techniques, and hybrid systems have all been investigated as viable answers to the problem of effectively deciphering Indian scripts, because they are famously challenging to write. To operate, these systems require pre-processing techniques, segmentation schemes, and language models. The outcomes of this methodical examination demonstrate that despite the fact that Hand Scanning for Indic script has advanced significantly, room still exists for advancement. Future research could focus on developing trustworthy models that can handle a range of writing styles and enhance accuracy using less-studied Indic scripts. This profession may advance with the creation of collected datasets and defined standards

Off-line Handwritten Kannada Text Recognition using Support Vector Machine using Zernike Moments

Abstract It is a well-known fact that building a character recognition system is one of the hottest areas of research as it is shown over the Internet and due to its wide range of prospects. The objective of this paper is to describe an OCR system for handwritten text documents in Kannada. The input to the system is a scanned image of a text and the output is a machine editable file compatible with most typesetting Kannada software. The system first extracts characters from the document image and a set of features are extracted from the character image using Zernike moments. The final 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

Traffic Signboard Recognition and Text Translation System using Word Spotting and Machine Learning

This project will help the non-native people of Karnataka to easily understand the kannada boards and travel easily. The main task of this work is to recognize the kannada traffic text boards and translate that to English language. Histogram equalization is used to find the gap between the characters. K-means clustering is used to divide the characters into different clusters then the segmented characters are passed to the pretrained model to recognize what the characters means. The model used for recognizing the traffic text is convolutional neural networks. The methodologies used here is the image augmentation, converting RGB image to grey scale and normalizing the image to reduce the noise. The validation accuracy obtained while training the model with coloured images, normalized image, grey scale image and normalized grey scale image is respectively 98.88%, 98.85%, 98.8% and 99.39% and while testing this model with kannada language, the testing accuracy obtained respectively with coloured images, normalized image, grey scale and normalized grey scale image is 95.91%, 96.58%, 95.42% and 96.98 % . In this work, word spotting method is employed for kannada language recognition. The performance of this system is faster since machine learning algorithms are used here

Offline Handwritten Kannada Numerals Recognition

Handwritten Character Recognition (HCR) is one of the essential aspect in academic and production fields. The recognition system can be either online or offline. There is a large scope for character recognition on hand written papers. India is a multilingual and multi script country, where eighteen official scripts are accepted and have over hundred regional languages. Recognition of unconstrained hand written Indian scripts is difficult because of the presence of numerals, vowels, consonants, vowel modifiers and compound characters. In this paper, recognition of handwritten Kannada numeral characters is implemented and the different Wavelet features are used as feature extraction in this paper. The zonal densities of different region of an image have been extracted in the database. The database consists of 50 samples of each Kannada numeral character. For classification, the K-Nearest Neighbor method is used. Recognition accuracy of 88% has been achieved

Probabilistic Neural Network based Approach for Handwritten Character Recognition

In this paper, recognition system for totally unconstrained handwritten characters for south Indian language of Kannada is proposed. The proposed feature extraction technique is based on Fourier Transform and well known Principal Component Analysis (PCA). The system trains the appropriate frequency band images followed by PCA feature extraction scheme. For subsequent classification technique, Probabilistic Neural Network (PNN) is used. The proposed system is tested on large database containing Kannada characters and also tested on standard COIL-20 object database and the results were found to be better compared to standard techniques

APPLICATION OF SVM AND SOFT FEATURES TO AZERBAIJANI TEXT RECOGNITION

The purpose of this study is to establish more accurate and less time consuming recognition system for Azerbaijani text recognition. The main problem of investigating and developing recognition systems is the extraction of features, in view of the fact that, most of current recognition systems use features, which are unintelligible for human mind and proposed for operating by computers. For eliminating abovementioned problem, in this paper was offered “soft” features, extracted on base of human-mind techniques. On the side of validating SVM approach and “soft” features provided in this paper, experiments were executed using various feature classes offered for Azerbaijani hand printed characters and different methods