MLPNN based handwritten character recognition using combined feature extraction

Katiyar, Gauri; Mehfuz, Shabana

doi:10.1109/ccaa.2015.7148550

Cited by 12 publications

(8 citation statements)

References 25 publications

(31 reference statements)

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“…This paper is an extension of previous work done by authors in the field of off-line handwritten character recognition [3] [4] [5].…”

Section: Introductionmentioning

confidence: 80%

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Katiyar¹

2017

AJNNA

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Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. Issues of character recognition system related to choice of classifiers and feature extraction methods can be resolved through these objectives. In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. The experiments have been performed using well known standard database acquired from CEDAR, also seven different approaches of feature extraction techniques have been proposed to construct the final feature vector. It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.

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“…This paper is an extension of previous work done by authors in the field of off-line handwritten character recognition [3] [4] [5].…”

Section: Introductionmentioning

confidence: 80%

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Katiyar¹

2017

AJNNA

Self Cite

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“…If the input modalities are dynamic or online, recognition is addressed in real-time. Offline recognition refers to delayed time recognition [1]. Offline recognition occurs when text is scanned and saved as an image on paper.…”

Section: Basic Of Character Recognitionmentioning

confidence: 99%

Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

Dhankhar¹,

Gupta²,

Memon³

et al. 2022

Computer Systems Science and Engineering

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In today's digital era, the text may be in form of images. This research aims to deal with the problem by recognizing such text and utilizing the support vector machine (SVM). A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language. A method is developed for identifying Hindi language characters that use morphology, edge detection, histograms of oriented gradients (HOG), and SVM classes for summary creation. SVM rank employs the summary to extract essential phrases based on paragraph position, phrase position, numerical data, inverted comma, sentence length, and keywords features. The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features. The second goal is to maintain or improve the classification system's performance. The experiment included news articles from various genres, such as Bollywood, politics, and sports. The proposed method's accuracy for Hindi character recognition is 96.97%, which is good compared with baseline approaches, and system-generated summaries are compared to human summaries. The evaluated results show a precision of 72% at a compression ratio of 50% and a precision of 60% at a compression ratio of 25%, in comparison to state-of-the-art methods, this is a decent result.

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“…The work of Chen [14] uses two smaller subsets of MNIST, one that had only digits 0 and 1 (12,665 samples for the trainset and 2115 for the testset) and the other only digits 3 and 5 (11,552 samples for the trainset and 1902 for the testset) and trained two feedforward NNs as a binary logistic regression model with the SGD training algorithm and batch size of 1. Furthermore, he conducted experiments by varying the trainset size from 5% to 100%.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the information from Table 6, we can conclude that our approach has reasons to be characterized as among the best, if not as the best. The approach in [11], although it declares slightly more than 2% better accuracy, it lacks the ability to recognize logic characters, which has added an extra level of difficulty to our approach. Furthermore, a more time-consuming feature extraction approach is used.…”

Section: Comparisonsmentioning

confidence: 99%

Optical Recognition of Handwritten Logic Formulas Using Neural Networks

et al. 2021

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In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.

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MLPNN based handwritten character recognition using combined feature extraction

Cited by 12 publications

References 25 publications

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

Optical Recognition of Handwritten Logic Formulas Using Neural Networks

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