CNN based common approach to handwritten character recognition of multiple scripts

Maitra, Durjoy Sen; Bhattacharya, Ujjwal; Parui, Susanta Kumar

doi:10.1109/icdar.2015.7333916

Cited by 127 publications

(56 citation statements)

References 21 publications

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“…Bhattacharya and Chaudhuri [13] investigated the use of wavelets and multilayer perceptron, in the identification of mixed Bangla, Devanagari and English numbers. Maitra et al [14] used the classical LeNet-5 CNN model in the identification of five kinds of handwritten numbers, namely, Bangla, Devanagari, English, Oriya, and Telugu. For Bangla handwritten digit recognition only, auto-encoder and deep convolutional neural network are both explored [15].…”

Section: Related Workmentioning

confidence: 99%

Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Jiang

Zhang

2020

IEICE Trans. Inf. & Syst.

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Handwritten numeral recognition is a classical and important task in the computer vision area. We propose two novel deep learning models for this task, which combine the edge extraction method and Siamese/Triple network structures. We evaluate the models on seven handwritten numeral datasets and the results demonstrate both the simplicity and effectiveness of our models, comparing to baseline methods.

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Section: Related Workmentioning

confidence: 99%

Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Jiang

Zhang

2020

IEICE Trans. Inf. & Syst.

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“…Although having fewer learnable parameters than the fully connected NN, CNN has a great performance advantage in the field of image data processing, in order to avoid the problems caused by over-fitting. But we still need a large number of training samples to train the CNN [43].…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Supervised Deep Learning in High Energy Phenomenology: a Mini Review*

Abdughani¹,

Ren²,

Wu³

et al. 2019

Commun. Theor. Phys.

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Deep learning, a branch of machine learning, have been recently applied to high energy experimental and phenomenological studies. In this note we give a brief review on those applications using supervised deep learning. We first describe various learning models and then recapitulate their applications to high energy phenomenological studies. Some detailed applications are delineated in details, including the machine learning scan in the analysis of new physics parameter space, the graph neural networks in the search of top-squark production and in the CP measurement of the top-Higgs coupling at the LHC.

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“…Domain knowledge that comes from experts is losing its importance when it comes to the design of visual recognition systems. CNNs have performed better on many standard handwriting recognition benchmarks [71][72][73][74][75][76][77][78], and it was observed to have a large margin compared to other types of techniques. With the help of deep learning techniques, several problems have been addressed in terms of image processing and understanding, including document image categorisation, handwritten recognition, and blind image quality assessment.…”

Section: Deep Learning and Its Applicationsmentioning

confidence: 99%

Deep Learning Algorithms for Arabic Handwriting Recognition: A Review

Al-Saffar¹,

Awang²,

Al-Saiagh³

et al. 2018

IJET

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Computer vision (CV) refers to the study of the computer simulation of human visual science. Major task of CV is to collect images (or video) so that they could be used for analysis, gathering information, and making decisions or judgements. CV has greatly progressed and developed in the past few decades. In recent years, deep learning (DL) approaches have won several contests in pattern recognition and machine learning. (DL) dramatically improved the state-of-the-art in visual object recognition, object detection, handwritten recognition and many other domains. Handwritten recognition technique is one of this tasks that targeted to extract the text from documents or another images type. In contrast to the English domain, there are a limited works on the Arabic language that achieved satisfactory results, Due to the Arabic language cursive nature that induces many technical difficulties. This paper highlighted the pre-processing and binarization methods that have been used in the literature along with proposed numerous directions for developing. We review the various current deep learning approaches and tools used for Arabic handwritten recognition (AHWR), identified challenges along this line of this research, and gives several recommendations including a framework based (DL) that is particularly applicable for dealing with cursive nature languages.

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CNN based common approach to handwritten character recognition of multiple scripts

Cited by 127 publications

References 21 publications

Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Supervised Deep Learning in High Energy Phenomenology: a Mini Review*

Deep Learning Algorithms for Arabic Handwriting Recognition: A Review

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