English Character Recognition Using Robust Back Propagation Neural Network

Zanwar, Shrinivas R.; Narote, A. S.; Narote, Sandipann P.

doi:10.1007/978-981-13-9187-3_20

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Gautam and Chai [14] Combination: Zoning method+zig-zag diagonal scan 94% Zanwar et al [16] Combination: Detached component analysis+hybrid PSO 98.25% Ibrahim et al [12] Combination: Features that are based on viewing capabilities+bit map feature. 97% Zanwar et al [15] Independent component analysis Second Group: Portion of the letters which belong to this group, precisely (small letter of L, Capital letter of I, and z) are not fully recognized successfully, the classifier misclassifies one sample from the testing set of samples (i.e., 23 from 24 testing sample scored). This is due to the common way of handwriting those letters, commonly capital letter of I is similarly written as a small letter of L, beside the used way of writing the capital letter of Z with an extra line in the middle which confused the first portion of the feature vector.…”

Section: Resultsmentioning

confidence: 99%

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Offline Handwritten English Alphabet Recognition (OHEAR)

Majeed

Nariman

2022

UHD J SCI TECH

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In most pattern recognition models, the accuracy of the recognition plays a major role in the efficiency of those models. The feature extraction phase aims to sum up most of the details and findings contained in those patterns to be informational and non-redundant in a way that is sufficient to fen to the used classifier of that model and facilitate the subsequent learning process. This work proposes a highly accurate offline handwritten English alphabet (OHEAR) model for recognizing through efficiently extracting the most informative features from constructed self-collected dataset through three main phases: Pre-processing, features extraction, and classification. The features extraction is the core phase of OHEAR based on combining both statistical and structural features of the certain alphabet sample image. In fact, four feature extraction portions, this work has utilized, are tracking adjoin pixels, chain of redundancy, scaled-occupancy-rate chain, and density feature. The feature set of 27 elements is constructed to be provided to the multi-class support vector machine (MSVM) for the process of classification. The OHEAR resultant revealed an accuracy recognition of 98.4%.

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Section: Resultsmentioning

confidence: 99%

“…The litterateurs of Zanwar et al [15] select 3410 samples of Chars74K which is another publicly available dataset. Independent component analysis (ICA) technique is used in feature extraction phase.…”

Section: Related Workmentioning

confidence: 99%

Offline Handwritten English Alphabet Recognition (OHEAR)

Majeed

Nariman

2022

UHD J SCI TECH

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“…A solution in character recognition proposed by Zanwar, et al [20] uses feature extraction before feeding the data into a backpropagation neural network system. Similar to the previously mentioned research, the data used in this test was handwritten English alphabets, with various shapes and pattern, selected from the Chars74K library.…”

Section: Figure 1 the Ina Ni Suratmentioning

confidence: 99%

Diagonal Based Feature Extraction and Backpropagation Neural Network in Handwritten Batak Toba Characters Recognition

Zamzami

Hayanti

Nababan

2021

KINETIK

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Handwritten character recognition is considered a complex problem since one’s handwritten character has its characteristics. Data used for this research was a photo of handwritten or scanned handwritten. In this research, Backpropagation Neural Network (BPNN) was used to recognize handwritten Batak Toba character, wherein preprocessing stage feature extraction was done using Diagonal Based Feature Extraction (DBFE) to obtain feature value. Furthermore, the feature value will be used as an input to BPNN. The total number of data used was190 data, where 114 data was used for the training process and another 76 data was used for testing. From the testing process carried out, the accuracy obtained was 87,19 %.

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