On-line Character Recognition Research Articles

Deep Convolutional Neural Network with a Stochastic Gradient Descent Optimizer (PDCNN-SGD) model for telugu character recognition

Telugu Character Recognition (TCR) has received significant attention because of the drastic increase in technological advancements such as multimedia, smartphones and iPods, and paper documents. Offline character recognition is the process of identifying Telugu characters from the scanned image or document whereas online character recognition enables to recognition of characters by the machine while the user writes. Several researchers have attempted to design online TCR models by the use of distinct classification models and feature extraction approaches. It is still necessary to construct automated and intelligent online TCR models, even if many studies have focused on offline TCR models. The Telugu character dataset construction and validation using an Inception and ResNet-based model are presented. The collection of 645 letters in the dataset includes 18 Achus, 38 Hallus, 35 Othulu, 34*16 Guninthamulu and 10 Ankelu. The proposed technique aims to efficiently recognize and identify distinctive Telugu characters online. This model's main preprocessing steps to achieve its goals include normalization, smoothing, and interpolation. Improved recognition performance can be attained by using Stochastic Gradient Descent (SGD) to optimize the model's hyperparameters.

The OnHW Dataset

This paper presents a handwriting recognition (HWR) system that deals with online character recognition in real-time. Our sensor-enhanced ballpoint pen delivers sensor data streams from triaxial acceleration, gyroscope, magnetometer and force signals at 100 Hz. As most existing datasets do not meet the requirements of online handwriting recognition and as they have been collected using specific equipment under constrained conditions, we propose a novel online handwriting dataset acquired from 119 writers consisting of 31,275 uppercase and lowercase English alphabet character recordings (52 classes) as part of the UbiComp 2020 Time Series Classification Challenge. Our novel OnHW-chars dataset allows for the evaluations of uppercase, lowercase and combined classification tasks, on both writer-dependent (WD) and writer-independent (WI) classes and we show that properly tuned machine learning pipelines as well as deep learning classifiers (such as CNNs, LSTMs, and BiLSTMs) yield accuracies up to 90 % for the WD task and 83 % for the WI task for uppercase characters. Our baseline implementations together with the rich and publicly available OnHW dataset serve as a baseline for future research in that area.

SVM and HMM Classifier Combination Based Approach for Online Handwritten Indic Character Recognition

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

Augmentation of Local, Global Feature Analysis for online Character Recognition System for Telugu Language using Feed Forward Neural Networks (FFNN)

In this paper, we propose ANN based online handwritten character recognition for Telugu Language. In literature review, it is observed that Size of the database and preprocessing approaches plays prominent role in the recognition performance. Preprocessing techniques like normalization, interpolation, Uniformization, Smoothing, Slant Correction and resampling techniques are performed for better recognition performance. Local features like(x,y)co-ordinates, (x,y) (2 x,2 y) and the global features like tan( ) are considered as features for ANN modeling and Classification of 52 Telugu vowels and consonants. Recognition performance is evaluated by augmentation the local, global features and and tan ( ) Features. Theperformance is evaluated in terms of precision, recall and F-measure. Significant Improvement is reported by augmentation andby adopting preprocessing techniques. The database used for the study is HP-online Telugu database.

Introduction of Balinese Script Handwriting Using Zoning and Multilayer Perceptron

Handwriting identification is one out of the many research ever conducted. In its development, the handwriting can be written in real time by the user by using the mouse (online character recognition). Various studies on the traditional character handwriting recognition continue to be developed. One of them is the recognition of the Balinese characters. Balinese characters have their own unique characters compared with the other regions. The difference between the shapes of the characters with the other characters are quite similar, or there are some characters that can only be distinguished by a small sketch or doodle.This study uses Artificial Neural Network with Backpropagation algorithm to perform the Balinese characters recognition and zoning as a method of feature extraction. In a variation of the extraction method, the characteristics used are Image Centroid and Zone (ICZ), Zone Centroid and Zone (ZCZ) and normalization of features. Of the three methods, it will be determined the best method used in the Balinese characters recognition.From the test results of the extraction method, the combined characteristics of the ICZ, ZCZ and normalization of features were the most effective to be used for the recognition of the Balinese characters. The level of accuracy obtained from the results of the online testing was 71,28% and 72,31% for offline testing, with parameters of Backpropagation, which used the value of learning rate of 0,03, a momentum value of 0,5 and the number of neurons in the hidden layer of 130.

Comparison Performance Evaluation of Modified Genetic Algorithm and Modified Counter Propagation Network for Online Character Recognition

This paper carries out performance evaluation of a Modified Genetic Algorithm (MGA) and Modified Counter Propagation Network (MCPN) Network for online character recognition. Two techniques were used to train the feature vectors using supervised and unsupervised methods. First, the modified genetic algorithm used feature selection to filter irrelevant features vectors and improve character recognition because of its stochastic nature. Second, MCPN has its capability to extract statistical properties of the input data. MGA and MCPN techniques were used as classifiers for online character recognition then evaluated using 6200-character images for training and testing with best selected similarity threshold value. The experimental results of evaluation showed that, at 5 x 7 pixels, MGA had 97.89% recognition accuracy with training time of 61.20ms while MCPN gave 97.44% recognition accuracy in a time of 62.46ms achieved. At 2480, MGA had 96.67% with a training time of 4.53ms, whereas MCPN had 96.33% accuracy with a time of 4.98ms achieved. Furthermore, at 1240 database sizes, MGA has 96.44 % recognition accuracy with 0.62ms training time whereas MCPN gave 96.11% accuracy with 0.75ms training time. The two techniques were evaluated using different performance metrics. The results suggested the superior nature of the MGA technique in terms of epoch, recognition accuracy, convergence time, training time and sensitivity.

Design and Implementation of Digital Medical Prescription Device

The project aim is to design a digital medical prescription device for doctors and medical professionals to enable the creation of hand written digital prescriptions in real time. By utilizing Tablet PC technology, doctors can create, edit and store the hand written prescriptions directly into a digital medium hence creating a patient record for future reference. The device has built-in Online Character Recognition software which is able to recognize the characters written by the doctor on the Touchscreen GUI. A doctor can study the patient prescription from history simply by entering the patient's medical ID.

A Statistical Framework for Online Arabic Character Recognition

ABSTRACTThe widely-used PDAs, touch screens, tablet-PCs are alternatives to keyboards with the advantages of being more friendly, easy, and natural. A framework for Arabic online character recognition is developed. The framework integrates the different phases of online Arabic text recognition. The used data poses several challenges such as delayed strokes handling, connectivity problems, variability, and style change of text. We process the delayed strokes at the different phases differently to improve the overall performance. This work includes feature extraction of many features, including several novel statistical features. Experimental results on challenging online Arabic characters show encouraging results.

Optimized Features for Genetic Based Neural Network Model for Online Character Recognition

Optimized Features for Genetic Based Neural Network Model for Online Character Recognition

A Modified Genetic based Neural Network Model for Online Character Recognition

A Modified Genetic based Neural Network Model for Online Character Recognition

Development of a Genetic based Neural Network System for Online Character Recognition

Character Recognition has been one of the most intensive research during the last few decades because of its potential applications. However, most existing classifiers used in recognizing online handwritten characters suffer from poor feature selection and slow convergence which affect training time and recognition accuracy. Hence, this paper focused on integrating an optimization (genetic algorithm) into modified backpropagation neural network to enhance the performance of character recognition. This paper proposed a methodology that is based on extraction of features using stroke number, invariant moments, projection and zoning. Genetic algorithm was use as feature selection to optimize the subset of the character for classification. A Modified Genetic Algorithm (MGA) was modified to reduce character recognition errors using fitness function and genetic operators. However, an integration of optimization algorithm (modified genetic algorithm) into an existing modified backpropagation (MOBP) learning algorithm was employed as classifier. For further enhancement of classifier, three classifiers (C1, C2 and C3) were formulated from MGA-MOBP model and evaluated using training time and correct recognition accuracy. C3 performed better than C1 and C2 in terms of convergence rate, correct recognition accuracy and feature selection (its ability to remove irrelevant features of character images). The results of the developed system achieved a false recognition of 0.56% and 99.44% overall recognition accuracy compared with existing models.

Online Kinect Handwritten Digit Recognition Based on Dynamic Time Warping and Support Vector Machine

Handwriting in-space from Kinect depth and color information is a challenging task due to the high variability of signature characteristics for difierent individuals. In this paper, a user-friendly human computer interaction system is proposed and implemented based on Kinect handwriting. The flngertip is flrstly tracked by our detection method in every depth frame to generate 3D trajectory of handwriting, and then normalization and smoothing are performed before feature extraction. On this basis, the time sequence feature of 3D signature can be captured as an online character recognition method, and a joint recognition framework is proposed based on DTW and SVM for input vectors of difierent lengths. The evaluation on a handwriting in-space dataset of digits from 0 to 9 shows that the proposed recognition scheme can ofier a high recognition accuracy and a satisfying robustness to noisy data in digit recognition test even with small training number. Therefore, the method can be successfully applied in many Human Computer Interaction applications in real world.

The Optical Character Recognition for Cursive Script Using HMM: A Review

Automatic Character Recognition has wide variety of applications such as automatic postal mail sorting, number plate recognition and automatic form of reader and entering text from PDA's etc. Cursive script's Automatic Character Recognition is a complex process facing unique issues unlike other scripts. Many solutions have been proposed in the literature to solve complexities of cursive scripts character recognition. This paper present a comprehensive literature review of the Optical Character Recognition (OCR) for off-line and on-line character recognition for Urdu, Arabic and Persian languages, based on Hidden Markov Model (HMM). We surveyed all most all significant approaches proposed and concluded future directions of OCR for cursive languages.

The optical character recognition of Urdu-like cursive scripts

We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image databases. For the sake of clarity, the various attempts are grouped into three parts, namely: (a) printed, (b) handwritten, and (c) online character recognition. Within each part, the works are analyzed par rapport a typical OCR pipeline with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition.

Global feature for online character recognition

This paper focuses on the importance of global features for online character recognition. Global features represent the relationship between two temporally distant points in a handwriting pattern. For example, it can be defined as the relative vector of two xy-coordinate features of two temporally separated points. Most existing online character recognition methods do not utilize global features, since their non-Markovian property prevents the use of the traditional recognition methodologies, such as dynamic time warping and hidden Markov models. However, we can understand the importance of, for example, the relationship between the starting and the ending points by attempting to discriminate “0” and “6”. This relationship cannot be represented by local features defined at individual points but by global features. Since O(N2) global features can be extracted from a handwriting pattern with N points, selecting those that are truly discriminative is very important. In this paper, AdaBoost is employed for feature selection. Experiments prove that many global features are discriminative and the combined use of local and global features can improve the recognition accuracy.

A Bayesian computational model for online character recognition and disability assessment during cursive eye writing

This research involves a novel apparatus, in which the user is presented with an illusion inducing visual stimulus. The user perceives illusory movement that can be followed by the eye, so that smooth pursuit eye movements can be sustained in arbitrary directions. Thus, free-flow trajectories of any shape can be traced. In other words, coupled with an eye-tracking device, this apparatus enables “eye writing,” which appears to be an original object of study. We adapt a previous model of reading and writing to this context. We describe a probabilistic model called the Bayesian Action-Perception for Eye On-Line model (BAP-EOL). It encodes probabilistic knowledge about isolated letter trajectories, their size, high-frequency components of the produced trajectory, and pupil diameter. We show how Bayesian inference, in this single model, can be used to solve several tasks, like letter recognition and novelty detection (i.e., recognizing when a presented character is not part of the learned database). We are interested in the potential use of the eye writing apparatus by motor impaired patients: the final task we solve by Bayesian inference is disability assessment (i.e., measuring and tracking the evolution of motor characteristics of produced trajectories). Preliminary experimental results are presented, which illustrate the method, showing the feasibility of character recognition in the context of eye writing. We then show experimentally how a model of the unknown character can be used to detect trajectories that are likely to be new symbols, and how disability assessment can be performed by opportunistically observing characteristics of fine motor control, as letter are being traced. Experimental analyses also help identify specificities of eye writing, as compared to handwriting, and the resulting technical challenges.

Freeman Code Based Online Handwritten Character Recognition for Malayalam using Backpropagation Neural Networks

Handwritten character recognition is conversion of handwritten text to machine readable and editable form. Online character recognition deals with live conversion of characters. Malayalam is a language spoken by millions of people in the state of Kerala and the union territories of Lakshadweep and Pondicherry in India. It is written mostly in clockwise direction and consists of loops and curves. The method aims at training a simple neural network with three layers using backpropagation algorithm. Freeman codes are used to represent each character as feature vector. These feature vectors act as inputs to the network during the training and testing phases of the neural network. The output is the character expressed in the Unicode format.

An Online Character Recognition System to Convert Grantha Script to Malayalam

This paper presents a novel approach to recognize Grantha, an ancient script in South India and converting it to Malayalam, a prevalent language in South India using online character recognition mechanism. The motivation behind this work owes its credit to (i) developing a mechanism to recognize Grantha script in this modern world and (ii) affirming the strong connection among Grantha and Malayalam. A framework for the recognition of Grantha script using online character recognition is designed and implemented. The features extracted from the Grantha script comprises mainly of time-domain features based on writing direction and curvature. The recognized characters are mapped to corresponding Malayalam characters. The framework was tested on a bed of medium length manuscripts containing 9-12 sample lines and printed pages of a book titled Soundarya Lahari writtenin Grantha by Sri Adi Shankara to recognize the words and sentences. The manuscript recognition rates with the system are for Grantha as 92.11%, Old Malayalam 90.82% and for new Malayalam script 89.56%. The recognition rates of pages of the printed book are for Grantha as 96.16%, Old Malayalam script 95.22% and new Malayalam script as 92.32% respectively. These results show the efficiency of the developed system.

Epistemic irrelevance in credal nets: The case of imprecise Markov trees

We focus on credal nets, which are graphical models that generalise Bayesian nets to imprecise probability. We replace the notion of strong independence commonly used in credal nets with the weaker notion of epistemic irrelevance, which is arguably more suited for a behavioural theory of probability. Focusing on directed trees, we show how to combine the given local uncertainty models in the nodes of the graph into a global model, and we use this to construct and justify an exact message-passing algorithm that computes updated beliefs for a variable in the tree. The algorithm, which is linear in the number of nodes, is formulated entirely in terms of coherent lower previsions, and is shown to satisfy a number of rationality requirements. We supply examples of the algorithm’s operation, and report an application to on-line character recognition that illustrates the advantages of our approach for prediction. We comment on the perspectives, opened by the availability, for the first time, of a truly efficient algorithm based on epistemic irrelevance.

フィードバックSOMを用いたオンライン文字認識の試み ―第2報 データ数に応じた学習法の検討について―

フィードバックSOMを用いたオンライン文字認識の試み ―第2報 データ数に応じた学習法の検討について―