Recognition of cursive Arabic handwritten text using embedded training based on HMMs

Rabi, Mouhcine; Amrouch, Mustapha; Mahani, Zouhir

doi:10.1016/j.jesit.2017.02.001

Cited by 24 publications

(9 citation statements)

References 14 publications

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“…General image pattern recognition systems are mainly composed of parts, including capture of picture images, picture presorting, extraction of image character, design of categorizer, and decision-making [13].…”

Section: Composition Of Image Recognition Technologymentioning

confidence: 99%

Application of Artificial Intelligence Recognition Technology in Digital Image Processing

Zhang¹

2022

Wireless Communications and Mobile Computing

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Synthetic Artificial Intelligence technique is a science and technique derived and developed on the basis of calculator application technology. Image recognition is a special image processing step that plays an important role. Only after image recognition can it enter the stage of picture analysis and understanding. With the development of various computer technologies, images have gradually become and have become an important source of information for people. The use of calculator artificial intelligence is becoming increasingly widespread; therefore, understanding its application and related research is more conducive to pointing out the direction of research and learning for us. The goal of this paper is to discuss the emergence and development of synthetic intelligence identification technology and analyze the application bottlenecks of various types of synthetic intelligence identification technology, so as to increase our understanding of Synthetic Artificial Intelligence technique and provide reference for the research in related fields. This article simply introduces the technology of artificial intelligence type and its new development trend, and by combining concrete images of public facilities, the application of different computer artificial recognition methods of image recognition processing on the basis of the traditional method is improved, and through the corresponding simulation software of processing and identification methods for the analysis and comparison, the main application of two methods, the image processing recognition error rate is less than 0.5; improving computer artificial intelligence identification technique for the analysis of its application in image processing has certain help. The preprocessing process generally includes image digitization, grayscale, binarization, noise removal, and character segmentation. In terms of image recognition, algorithms mainly include statistical recognition, syntax recognition, and template matching. In recent years, with the development of neural networks and support vector machine technology, image recognition technology has a new and higher level of development.

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Section: Composition Of Image Recognition Technologymentioning

confidence: 99%

Application of Artificial Intelligence Recognition Technology in Digital Image Processing

Zhang¹

2022

Wireless Communications and Mobile Computing

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“…These techniques include both deep and shallow architectures. Mouhcine et al, [24] proposed a hidden Markova model (HMM) for the recognition of handwritten cursive Arabic text. Marie-Sainte and Alyani used the Firefly algorithm for the classification of Arabic text [25].…”

Section: Classification Techniquesmentioning

confidence: 99%

Deep Learning Based Pashto Characters Recognition

Khan

Nazir

2022

PPASA

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In artificial intelligence, text identification and analysis that are based on images play a vital role in the text retrieving process. Automatic text recognition system development is a difficult task in machine learning, but in the case of cursive languages, it poses a big challenge to the research community due to slight changes in character’s shapes and the unavailability of a standard dataset. While this recognition task becomes more challenging in the case of Pashto language due to a large number of characters in its dataset than other similar cursive languages (Persian, Urdu, Arabic) and a slight change in character’s shape. This paper aims to address accept these challenges by developing an optimal optical character recognition (OCR) system to recognise isolated handwritten Pashto characters. The proposed OCR system is developed using multiple long short-term memory (LSTM) based deep learning model. The applicability of the proposed model is validated by using the decision trees (DT) classification tool based on the zoning feature extraction technique and the invariant moment approaches. An overall accuracy rate of 89.03% is calculated for the multiple LSTM-based OCR system while DT-based recognition rate of 72.9% is achieved using zoning feature vector and 74.56% is achieved for invariant moments-based feature map. Applicability of the system is evaluated using different performance metrics of accuracy, f-score, specificity, and varying training and test sets.

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“…[29] achieved a WER of 21.95% for "abc-d" despite the application of skew correction, noise reduction, size normalization and line extraction for preprocessing. In [47], the contribution was at the HMM level. The statistical and geometrical extracted features were preceded by baseline estimation.…”

Section: Comparison With State-of-art Systemsmentioning

confidence: 99%

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

et al. 2021

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One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST).

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Recognition of cursive Arabic handwritten text using embedded training based on HMMs

Cited by 24 publications

References 14 publications

Application of Artificial Intelligence Recognition Technology in Digital Image Processing

Application of Artificial Intelligence Recognition Technology in Digital Image Processing

Deep Learning Based Pashto Characters Recognition

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

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