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.

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

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 2018. 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 142 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.

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research.

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A Review on Human-Computer Interaction and Intelligent Robots

What do you do to start reading optical pattern recognition? Searching the book that you love to read first or find an interesting book that will make you want to read? Everybody has difference with their reason of reading a book. Actuary, reading habit must be from earlier. Many people may be love to read, but not a book. It's not fault. Someone will be bored to open the thick book with small words to read. In more, this is the real condition. So do happen probably with this optical pattern recognition.

Optical Pattern Recognition.

Some recent research reports that a dendritic neuron model (DNM) can achieve better performance than traditional artificial neuron networks (ANNs) on classification, prediction, and other problems when its parameters are well-tuned by a learning algorithm. However, the back-propagation algorithm (BP), as a mostly used learning algorithm, intrinsically suffers from defects of slow convergence and easily dropping into local minima. Therefore, more and more research adopts non-BP learning algorithms to train ANNs. In this paper, a dynamic scale-free network-based differential evolution (DSNDE) is developed by considering the demands of convergent speed and the ability to jump out of local minima. The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem. Nine meta-heuristic algorithms are applied into comparison, including the champion of the 2017 IEEE Congress on Evolutionary Computation (CEC2017) benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR). The experimental results reveal that DSNDE achieves better performance than its peers.

Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution

Optical Character Recognition Systems for Different Languages with Soft Computing

Optical character recognition (OCR) is process of classification of optical patterns contained in a digital image. The character recognition is achieved through segmentation, feature extraction and classification. This chapter presents the basic ideas of OCR needed for a better understanding of the book. The chapter starts with a brief background and history of OCR systems. Then the different techniques of OCR systems such as optical scanning, location segmentation, pre-processing, segmentation, representation, feature extraction, training and recognition and post-processing. The different applications of OCR systems are highlighted next followed by the current status of the OCR systems. Finally, the future of the OCR systems is presented.

Optical Character Recognition Systems

The optical character recognition (OCR) systems for Hindi language were the most primitive ones and occupy a significant place in pattern recognition. The Hindi language OCR systems have been used successfully in a wide array of commercial applications. The different challenges involved in the OCR systems for Hindi language is investigated in this Chapter. The pre-processing activities such as binarization, noise removal, skew detection, character segmentation and thinning performed on the datasets considered. The feature extraction is performed through fuzzy Hough transform. The feature based classification is performed through important soft computing techniques viz rough fuzzy multilayer perceptron (RFMLP), fuzzy support vector machine (FSVM), fuzzy rough support vector machine (FRSVM) and fuzzy markov random fields (FMRF). The superiority of soft computing techniques is demonstrated through the experimental results.

Optical Character Recognition Systems for Hindi Language

The optical character recognition (OCR) systems for English language were the most primitive ones and occupy a significant place in pattern recognition. The English language OCR systems have been used successfully in a wide array of commercial applications. The different challenges involved in the OCR systems for English language is investigated in this chapter. The pre-processing activities such as binarization, noise removal, skew detection and correction, character segmentation and thinning are performed on the datasets considered. The feature extraction is performed through discrete cosine transformation. The feature based classification is performed through important soft computing techniques viz fuzzy multilayer perceptron (FMLP), rough fuzzy multilayer perceptron (RFMLP), fuzzy support vector machine (FSVM) and fuzzy rough support vector machine (FRSVM). The superiority of soft computing techniques is demonstrated through the experimental results.

Optical Character Recognition Systems for English Language

The continuous increase in demand to discover robust and low cost optical character recognition (OCR) systems has prompted researchers to look for rigorous methods of character recognition. In the past OCR systems have been built through traditional pattern recognition and machine learning approaches. There has always been a quest to develop best OCR products which satisfy the user’s needs. Since past few decades soft computing techniques have come up as a promising candidate for the development of cost effective OCR systems. Some important soft computing techniques for optical character recognition (OCR) systems are presented in this chapter. They are hough transform for fuzzy feature extraction, genetic algorithms (GA) for feature selection, fuzzy multilayer perceptron (FMLP), rough fuzzy multilayer perceptron (RFMLP), fuzzy support vector machine (FSVM), fuzzy rough versions of support vector machine (FRSVM), hierarchical fuzzy bidirectional recurrent neural networks (HFBRNN) and fuzzy markov random fields (FMRF). These techniques are used for developing OCR systems for different languages viz English, French, German, Latin, Hindi and Gujrati languages. The soft computing methods are used in the different steps of OCR systems discussed in Chap. 2. A comprehensive assessment of these methods is performed in Chaps. 4– 9 for the stated languages. A thorough understanding of this chapter will help the readers to appreciate the reading material presented in the abovementioned chapters.

Krupa Mandaviya

Papers

Optical Character Recognition Systems for Different Languages with Soft Computing

Optical Character Recognition Systems

Optical Character Recognition Systems for Hindi Language

Optical Character Recognition Systems for English Language

Soft Computing Techniques for Optical Character Recognition Systems