Handwriting Recognition System -- A Survey

Yadav, Pooja; Popli, Neha

doi:10.2139/ssrn.2441045

Cited by 4 publications

(6 citation statements)

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“…The LSE process is a form of mathematical regression analysis to determine the best fit for several data and to demonstrate the relationship of the data points visually. Every data point represented the relation between a known independent and an unknown variable, as is shown in equation (1).…”

Section: A Segments Interpolation (Si)mentioning

confidence: 99%

“…It captures anything that may affect others. We use a sample to find the estimated regression line from equation (1). The sample is showing in equation (2).…”

Section: A Segments Interpolation (Si)mentioning

confidence: 99%

“…BACKGROUNDHandwriting recognition is a dynamic model and simulation environment that is considered a part of pattern recognition. It can contribute an essential benefit to our real life [1]. The diversity of handwriting recognition comes with extensive usage of a massive number of costly computational aspects.…”

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confidence: 99%

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Segments Interpolation Extractor for Finding the Best Fit Line in Arabic Offline Handwriting Recognition Words

et al. 2021

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In the last few years, deep learning-based models have made significant inroads into the field of handwriting recognition. However, deep learning requires the availability of massive labelled data and considerable computation for training or automatic feature extraction. The role of handcrafted features and their significance is still crucial for a specific language type because it is a unique way of writing the characters. These are primitive segments that describe the letter horizontally or vertically distinguish an Arabic letter. This article develops a new type of feature for handwriting using Segments Interpolation (SI) to find the best fitting line in each of the windows and build a model for finding the best operating point window size for SI features. The experimental design was done on two subsets of the Institute for Communications Technology/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT) database. The first one contains 10 classes (C10), and the second one has 22 classes (C22). The extracted features were trained with Support Vector Machine (SVM) and Extreme Learning Machine (ELM) with different kernels and activation functions. The evaluation metrics from a classification perspective (Accuracy, Precision, Recall and Fmeasure) were applied. As a result, SI shows significant results with SVM 90.10% accuracy for C10 and 88.53% accuracy for C22.INDEX TERMS Arabic handwriting word recognition, classification, ELM, feature extraction, Segments Interpolation and SVM. I. BACKGROUNDHandwriting recognition is a dynamic model and simulation environment that is considered a part of pattern recognition. It can contribute an essential benefit to our real life [1]. The diversity of handwriting recognition comes with extensive usage of a massive number of costly computational aspects. Currently, the technology provides an exceptionally smooth technique and, at the same time, hides the bright side of handwriting text. Several applications where handwriting recognition is necessary, such as bank cheques [2], postal addresses [3], and handwritten form processing [4].Numerous studies on handwriting recognition, especially for the Latin script [2][3], have been conducted over the last few decades. There are quite good results for machine printed text recognition with over 99% accuracy, for instance. However, in Arabic handwriting recognition as against Latin [7], only minor studies have been carried out. Due to the intricacy of Arabic text and insufficient databases about this language [8]. Recognition of Arabic text is in the initial phases compared to the recognition of Chinese, Latin, and Japanese manuscripts. Furthermore, there are several challenges in Arabic writing recognition practices from the data's cursive form. These challenges arise due to several aspects, like the Arabic writing setup that is cursive, the pen, the writing style, and other elements.Arabic handwriting recognition consists of two classes, online and offline [9], respectively. First, the characteristics of This work is licensed under a Creativ...

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Section: A Segments Interpolation (Si)mentioning

confidence: 99%

“…It captures anything that may affect others. We use a sample to find the estimated regression line from equation (1). The sample is showing in equation (2).…”

Section: A Segments Interpolation (Si)mentioning

confidence: 99%

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Segments Interpolation Extractor for Finding the Best Fit Line in Arabic Offline Handwriting Recognition Words

et al. 2021

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“…According to another research study [13], different techniques are available to recognize hand written documents. These algorithms can be used to recognize many more languages like hindi, Punjabi, etc.…”

Section: Application Of Neural Network Algorithms For Hwdrmentioning

confidence: 99%

A Survey on using Neural Network based Algorithms for Hand Written Digit Recognition

Ramzan¹,

Khan²,

Awan³

et al. 2018

ijacsa

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The detection and recognition of handwritten content is the process of converting non-intelligent information such as images into machine edit-able text. This research domain has become an active research area due to vast applications in a number of fields such as handwritten filing of forms or documents in banks, exam form filled by students, users' authentication applications. Generally, the handwritten content recognition process consists of four steps: data preprocessing, segmentation, the feature extraction and selection, application of supervised learning algorithms. In this paper, a detailed survey of existing techniques used for Hand Written Digit Recognition(HWDR) is carried out. This review is novel as it is focused on HWDR and also it only discusses the application of Neural Network (NN) and its modified algorithms. We discuss an overview of NN and different algorithms which have been adopted from NN. In addition, this research study presents a detailed survey of the use of NN and its variants for digit recognition. Each existing work, we elaborate its steps, novelty, use of dataset and advantages and limitations as well. Moreover, we present a Scientometric analysis of HWDR which presents top journals and sources of research content in this research domain. We also present research challenges and potential future work.

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“…Pooja yadav, Nidhika Yadav presented [5] an algorithm can be used for recognize Hindi, Punjabi, Urdu and many more languages. We can use Back propagation algorithm to improve the efficiency and correctness of the algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Review on Comparison of Handwritten digit recognition using ANN Models FFNN and RNN

Naik

2017

International Journal of Recent Advancement in Engineering &Amp; Research

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-Handwritten digit recognition is used for communicating and recording information. [6] Machine recognition of handwriting has practical implementations like reading handwritten notes in PDA, postal address on envelopes, amounts in bank cheques, handwritten fields in forms etc. This paper describes the work done on MNIST dataset using FFNN (Feed forward Neural network) method and RNN(Recurrent Neural Network) and the difference in their results.

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Handwriting Recognition System -- A Survey

Cited by 4 publications

References 0 publications

Segments Interpolation Extractor for Finding the Best Fit Line in Arabic Offline Handwriting Recognition Words

Segments Interpolation Extractor for Finding the Best Fit Line in Arabic Offline Handwriting Recognition Words

A Survey on using Neural Network based Algorithms for Hand Written Digit Recognition

Review on Comparison of Handwritten digit recognition using ANN Models FFNN and RNN

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