A Generic OCR Using Deep Siamese Convolution Neural Networks

Sokar, Ghada; Hemayed, Elsayed E.; Rehan, M.

doi:10.1109/iemcon.2018.8614784

Cited by 17 publications

(15 citation statements)

References 15 publications

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“…Researchers have also used Convolutional Neural Network (CNN) in conjunction with other techniques for the recognition of characters. These techniques have been applied on different datasets to check the accuracy of techniques [74], [87], [144]- [146].…”

Section: B Farsi / Persian Scriptmentioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

359

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Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

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Section: B Farsi / Persian Scriptmentioning

confidence: 99%

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Memon

Sami

Khan³

et al. 2020

IEEE Access

359

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning has been received much attention from researchers [23] [24]. The authors in [24] deployed a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC).…”

Section: Related Workmentioning

confidence: 99%

“…In [23], the authors presented a generic OCR system based on deep Siamese convolution neural networks (CNNs) and support vector machines (SVM). Supervised deep CNNs achieve a high level of accuracy in classification tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, one characteristic of the Hausdorff distance is that it heavily punishes single outliers, which is a severe drawback in many cases [40]. To verify the efficiency of the proposed model compared to methods that rely on the use of deep neural networks as one of the most famous tools for extracting features, another set of experiments was conducted to compare the proposed model with recent works in [23] and [24] that differ for each other in the type of CNN and classifier as illustrated in Table IX. Both methods were re-implemented and running on the PATS-A01 dataset.…”

Section: Experiments 6: Comparative Studymentioning

confidence: 99%

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An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

Darwish

Elzoghaly

2020

IEEE Access

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In the recent few years, there was a concentrated search on Arabic Optical Character Recognition (OCR), especially the recognition of scanned, offline, machine-printed documents. However, Arabic OCR consequences are dissatisfying and are still a developed research area. Finding the best feature extraction techniques and selecting an appropriate classification algorithm lead to supreme recognition accuracy and low computational overhead. This paper presents a new Arabic OCR model by integrating both of Genetic Algorithm (GA) and the Fuzzy K-Nearest Neighbor classifier (F-KNN) in a unified framework to enhance the identification accuracy. GA is utilized as a feature selection algorithm that has better convergence and spread of solutions with candid variation preservation mechanism. The F-KNN algorithm is more appropriate to classify ambiguous or uncertain data objects in the sense that every object belongs to all classes with different degrees of membership. The suggested model semantically fuses bio-inspired based feature vectors with fuzzy KNN classifier to build accurate membership function for each class. Experimental results compared to other approaches revealed the effectiveness of the suggested model and demonstrated that the feature selection approach increased the identification accuracy process.

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“…Optical character recognition (OCR) is an important research field in the field of pattern recognition [1,2]. It combines digital image processing, computer graphics and artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Fast identification method for express end sorting label code based on convolutional recurrent neural network

et al. 2020

SIViP

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In the automatic sorting process of express, the express end sorting label code is used to indicate that the express is dispatched to a specific address by a specific courier. Since there are many areas on the express bill containing digital information, some areas may be improperly photographed, etc. The difficulty in positioning and recognizing the express end sorting label code region is increased. To solve this problem, this paper proposes an express end sorting label code recognition method with convolutional recurrent neural network for the code specification, which has certain versatility. In order to improve the overall code recognition speed, this paper optimizes the traditional digital recognition method, removes the original segmentation operation of the character and recognizes the code as sequence recognition. Firstly, the coding region is located, and then, the express end sorting label code is recognized by the convolutional recurrent neural network. In order to test the experimental performance, this paper tests on Free-Type dataset and SUN-synthesized dataset. The experimental results show that the proposed method improves the recognition accuracy and processing speed of the express end sorting label code.

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A Generic OCR Using Deep Siamese Convolution Neural Networks

Cited by 17 publications

References 15 publications

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

Fast identification method for express end sorting label code based on convolutional recurrent neural network

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