Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-etriby, Sherif

doi:10.3390/s16030346

Cited by 7 publications

(5 citation statements)

References 40 publications

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“…Pattern recognition includes techniques such as speech recognition, which recognizes and extracts human voices from voice data and interprets them as commands in optical character recognition (OCR), where characters from image data are recognized and converted into text data and a full-text search system, which recognizes specific keywords and searches documents from a large amount of document information [22,23].…”

Section: Pattern Recognitionmentioning

confidence: 99%

Development and Practical Applications of Computational Intelligence Technology

Matsuzaka,

Yashiro

2024

BioMedInformatics

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Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.

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Section: Pattern Recognitionmentioning

confidence: 99%

Development and Practical Applications of Computational Intelligence Technology

Matsuzaka,

Yashiro

2024

BioMedInformatics

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“…In [42], the authors proposed a system synthesizing Arabic handwritten words and text pages to generate comprehensive databases for training and validating OCR systems. In the database, vocabulary of the 50,000 most-common Arabic words are used for error correction.…”

Section: Datasetmentioning

confidence: 99%

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

et al. 2023

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Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.

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“…en, backpropagation neural network, statistical pattern recognition, and support vector machine (SVM) methods [13] are used to recognize the characters or strokes. Finally, the individual characters or strokes are combined into characters according to specific rules.…”

Section: Manchu Language Ocrmentioning

confidence: 99%

OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

Zhang

Liu

Wang

et al. 2021

Scientific Programming

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Manchu is a low-resource language that is rarely involved in text recognition technology. Because of the combination of typefaces, ordinary text recognition practice requires segmentation before recognition, which affects the recognition accuracy. In this paper, we propose a Manchu text recognition system divided into two parts: text recognition and text retrieval. First, a deep CNN model is used for text recognition, using a sliding window instead of manual segmentation. Second, text retrieval finds similarities within the image and locates the position of the recognized text in the database; this process is described in detail. We conducted comparative experiments on the FAST-NU dataset using different quantities of sample data, as well as comparisons with the latest model. The experiments revealed that the optimal results of the proposed deep CNN model reached 98.84%.

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Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

Cited by 7 publications

References 40 publications

Development and Practical Applications of Computational Intelligence Technology

Development and Practical Applications of Computational Intelligence Technology

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

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