SVM-Based Segmentation-Verification of Handwritten Connected Digits Using the Oriented Sliding Window

Gattal, Abdeljalil; Chibani, Youcef

doi:10.1142/s1469026815500054

Cited by 16 publications

(11 citation statements)

References 33 publications

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“…to recognize unconstrained numerical strings including four stages: pre-processing, segmentation, feature extraction, and classification [7]- [15]. The most challenging step in such systems is the segmentation stage, which segments an input string image into multiple isolated digits.…”

Section: Introductionmentioning

confidence: 99%

Unknown-Length Handwritten Numeral String Recognition Using Cascade of PCA-SVMNet Classifiers

Aly

Mohamed

2019

IEEE Access

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Automatic recognition of handwritten digit string with unknown length has many potential real applications. The most challenging step in this problem is how to efficiently segment connected and/or overlapped digits exhibited in the input image. Most existing numeral string segmentation approaches combine several segmentation hypotheses to handle various types of connected digits. This paper proposes a new handwritten digit string recognition without applying any explicit segmentation techniques. The proposed method uses a new cascade of hybrid principal component analysis network (PCANet) and support vector machine (SVM) classifier called PCA-SVMNet. PCANet is an emerging unsupervised simple deep neural network typically with only two convolutional layers. The proposed PCA-SVMNet model adds a new fully connected layer trained separately using SVM optimization method. Cascaded stages of PCA-SVMNet classifiers are constructed and trained to recognize various types of isolated and connected digits. Every PCA-SVMNet classifier is trained separately using combinations of real and synthetic touching digits. The first 1D-PCA-SVMNet stage is trained to recognize isolated handwritten digits (0. .. 9) while forwarding non-isolated digits to the next stages. Each of the following stages is designed to recognize a class of connected digits and forwards the higher class to its successor. Multiple stages can be added accordingly to classify more complex touching digits. The experimental results using NIST SD19 real dataset show that the cascade of PCA-SVMNet classifier efficiently recognizes unknown handwritten digit string without applying any sophisticated segmentation methods. The proposed method achieves state-of-the-art recognition accuracy compared to other segmentation-free techniques.

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Section: Introductionmentioning

confidence: 99%

Unknown-Length Handwritten Numeral String Recognition Using Cascade of PCA-SVMNet Classifiers

Aly

Mohamed

2019

IEEE Access

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“…These points are derived through the decision line from the deep points in the image. Gattal et al [33] extended the research by combining different segmentation approaches based on configuration links between overlapped digits. They have used the sliding window Radon transform of these segmentation techniques to take the decision about selecting or discarding a digit image.…”

Section: Related Workmentioning

confidence: 99%

Deep Wavelet Neural Network based Robust Text Recognition for Overlapping Characters

Tripathi¹,

Patheja²

2021

IJACSA

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This paper presents a deep learning based intelligent text recognition system with touching and overlapped characters. The robustness and effectiveness in the proposed model are enhanced through the modified configuration of neural network known as Deep Wavelet Neural Network (DWNN). The capability of deep learning networks to learn efficiently from an unlabeled dataset has attracted the attention of many researchers over the last decade. However, the performance of these networks is subject to the quality of the dataset and invariant image representation. Numerous optical character recognition techniques have also been presented in the recent years, but the overlapped and touching characters have not been addressed much. The nonlinear and uncertain representation of image data in case of overlapped text adds severe complexity in the process of feature extraction and respective learning. The proposed architecture of DWNN uses fast decaying wavelet functions as activation function in place of conventional sigmoid function to cope up with the uncertainties and nonlinearity of the data representation in overlapped text images. It comprises of cascaded layered architecture of translated and dilated versions of wavelets as activation functions for the training and feature extraction at multiple levels. The local transformation and deformation variation in the visual data has also been taken care efficiently through the modified architecture of DWNN. Comprehensive experimental analysis has been performed over various test images to verify the effectiveness of the proposed text recognition system. The performance of the proposed method is assessed with the help of the metrics, namely, estimation error, cost function and accuracy. The proposed approach will be implemented in MATLAB.

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“…Neural network approaches and feature extraction techniques for handwritten digits recognition. The main objective is to achieve accuracy and high recognition rate [9]. The performance of two Artificial Neural Network Models Feed Forward Neural Network (FFNN) and Recurrent Neural network(RNN) for the HWDR by using the MNIST dataset, is compared by the researchers in which the output shows that the RNN is better in the right recognition of the digits.…”

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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SVM-Based Segmentation-Verification of Handwritten Connected Digits Using the Oriented Sliding Window

Cited by 16 publications

References 33 publications

Unknown-Length Handwritten Numeral String Recognition Using Cascade of PCA-SVMNet Classifiers

Unknown-Length Handwritten Numeral String Recognition Using Cascade of PCA-SVMNet Classifiers

Deep Wavelet Neural Network based Robust Text Recognition for Overlapping Characters

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

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