Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

Butt, Hanan; Raza, Muhammad Raheel; Ramzan, Muhammad; Ali, Muhammad Junaid; Haris, Muhammad

doi:10.3390/forecast3030033

Cited by 39 publications

(24 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…erefore, the purpose of this study is to apply recurrent neural networks in deep learning techniques to the project cost prediction of park landscapes, so that the construction cost of park landscapes can be better controlled. With the proposal of the deep learning theory and the improvement of computing performance of hardware devices, recurrent neural network (RNN) technology has gained great success in the fields of computer vision and natural language processing with a high recognition rate [23][24][25][26][27][28]. erefore, this paper attempts to introduce RNN into the cost prediction of the park landscape project in order to further improve the accuracy of cost prediction.…”

Section: Introductionmentioning

confidence: 99%

Research on the Predictive Analysis of Park Landscape Design and Cost Based on RNN Model

Lyu¹,

Dan²,

Liu³

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

As people’s awareness of the environment gradually increases and their requirements for the comfort of living space become higher, landscape design has also ushered in a golden period of development. With the increasing investment in landscape construction in urban development, the area of park green space has been increasing. A park is a place that provides recreation and relaxation for the public. However, the mere pursuit of landscape quality and artistic effects without effective cost control will eventually lead to a rise in construction costs. Therefore, this study explores the main influencing factors that lead to high park landscape costs by analyzing the current development of park landscape design. Based on the comprehensive analysis, a park landscape cost prediction model based on recurrent neural networks is proposed in order to better control the construction costs of park landscapes. This study applies advanced deep learning technology to the project management of park landscapes, which effectively improves the accuracy of cost prediction. In addition, an artificial bee colony algorithm is introduced to update the weights of the recurrent neural network, resulting in a globally optimal ABC-RNN prediction model. The experimental results show that the proposed ABC-RNN prediction model has higher prediction accuracy and stability than the commonly used prediction models.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on the Predictive Analysis of Park Landscape Design and Cost Based on RNN Model

Lyu¹,

Dan²,

Liu³

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Finally, Fuzzy K-NN classifier is used to predict class label. (Butt et al, 2021) recognized Arabic script from a video stream. Recurrent Neural Networks (RNNs) and CNNs were to implement the approach.…”

Section: Related Workmentioning

confidence: 99%

A Printed Arabic Optical Character Recognition System using Deep Learning

Alghyaline¹

2022

Journal of Computer Science

View full text Add to dashboard Cite

Recognizing Arabic script is challenging for many reasons: The Arabic language is cursive and morphologically rich. There is a high similarity between Arabic letters. Moreover, Arabic has many diacritics and dots, and they change the letter's phonetic transcription. This study proposes a Printed Arabic Optical Character Recognition approach (PAOCR) based on the state-of-the-art You Only Look Once (YOLO) object detector. Four techniques were proposed and implemented to design an end-to-end Arabic OCR system. First, the YOLO4 object detector is customized and trained on deep Convolutional Neural Networks (CNNs) to recognize Arabic characters. Second, the overlapped bounding boxes are processed to keep the most accurate box for each character. Third, the Hunspell library checks the word spelling and corrects the wrong ones. Fourth, edit distance is used to compare OCR misspelled words with Hunspell's suggestions and choose the closest correct word. The experimental results in the Arabic Printed Text Image (APTI) dataset showed that the Word Recognition Rate (WRR) of customized YOLO4 Arabic OCR is 66.7%, whereas applying the three proposed techniques with YOLO4 achieved 82.4%. A comparison with two existing OCR systems Tesseract and ABBYY showed that the proposed approach achieved a 95.7% Character Recognition Rate (CRR), whereas Tesseract and ABBYY achieved 92.3 and 84.8%, respectively.

show abstract

“…Deep learned features showed superior results compared with handcrafted features during the last decade in terms of recognition accuracy and speed in many fields of image processing [7,8]. Deep learned-based OCR systems use Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN) for feature extraction and classification [9,10]. The CNN architecture consists of a sequence of layers.…”

Section: Introductionmentioning

confidence: 99%

Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

This study aims to review the latest contributions in Arabic Optical Character Recognition (OCR) during the last decade, which helps interested researchers know the existing techniques and extend or adapt them accordingly. The study describes the characteristics of the Arabic language, different types of OCR systems, different stages of the Arabic OCR system, the researcher's contributions in each step, and the evaluation metrics for OCR. The study reviews the existing datasets for the Arabic OCR and their characteristics. Additionally, this study implemented some preprocessing and segmentation stages of Arabic OCR. The study compares the performance of the existing methods in terms of recognition accuracy. In addition to researchers' OCR methods, commercial and open-source systems are used in the comparison. The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters. Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment, and few approaches were tested on pagelevel scripts. Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low, under 75% for printed text, and further improvement is needed. Moreover, most of the current approaches are offline OCR systems, and there is no remarkable contribution to online OCR systems.

show abstract

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

Cited by 39 publications

References 67 publications

Research on the Predictive Analysis of Park Landscape Design and Cost Based on RNN Model

Research on the Predictive Analysis of Park Landscape Design and Cost Based on RNN Model

A Printed Arabic Optical Character Recognition System using Deep Learning

Arabic Optical Character Recognition: A Review

Contact Info

Product

Resources

About