MC-OCR Challenge 2021: Deep Learning Approach for Vietnamese Receipts OCR

Bui, Doanh C.; Truong, Dung; Vo, Nguyen D.; Nguyen, Khang

doi:10.1109/rivf51545.2021.9642128

Cited by 17 publications

(3 citation statements)

References 15 publications

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“…Fully Convolutional Neural Networks (FCNs) are notable for their absence of fully connected layers, enhancing efficiency in image processing tasks such as fake fingerprint detection by offering high accuracy, faster processing, and reduced memory needs [74,75]. Region Proposed Networks (RPNs), including models like Faster R-CNN, are applied in object detection in diverse areas such as extracting information from receipts, recognizing handwritten text, and even identifying mathematical expressions in documents [76][77][78]. Figure 5 shows the basic architecture used in CNN.…”

Section: Crack Detection Using Deep Learning Methodsmentioning

confidence: 99%

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

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Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

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Section: Crack Detection Using Deep Learning Methodsmentioning

confidence: 99%

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The Transformer model is an excellent solution to this type kind of problem. In recent years, the transformer model has been widely applied in computer vision tasks and has shown excellent results, such as in Optical Character Recognition (OCR) task [39]. Apart from OCR tasks, transformer models have also shown excellent results in video understanding tasks [40].…”

Section: ) Transformer Modelmentioning

confidence: 99%

An End-to-End Air Writing Recognition Method Based on Transformer

Tan,

Tong,

Matsumaru

et al. 2023

IEEE Access

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The air-writing recognition task entails the computer's ability to directly recognize and interpret user input generated by finger movements in the air. This form of interaction between humans and computers is considered natural, cost-effective, and immersive within the domain of human-computer interaction (HCI). While conventional air-writing recognition has primarily focused on recognizing individual characters, a recent advancement in 2022 introduced the concept of writing in the air (WiTA) to address continuous airwriting tasks. In this context, we assert that the Transformer-based approach can offer improved performance for the WiTA task. To solve the WiTA task, this study formulated an end-to-end air-writing recognition method called TR-AWR, which leverages the Transformer model. Our proposed method adopts a holistic approach by utilizing video frame sequences as input and generating letter sequences as outputs. To enhance the performance of the WiTA task, our method combines the vision transformer model with the traditional transformer model, while introducing data augmentation techniques for the first time. Our approach achieves a character error rate (CER) of 29.86% and a decoding frames per second (D-fps) value of 194.67 fps. Notably, our method outperforms the baseline models in terms of recognition accuracy while maintaining a certain level of real-time performance. The contributions of this paper are as follows: Firstly, this study is the first to incorporate the Transformer method into continuous air-writing recognition research, thereby reducing overall complexity and attaining improved results. Additionally, we adopt an end-to-end approach that streamlines the entire recognition process. Lastly, we propose specific data augmentation guidelines tailored explicitly for the WiTA task. In summary, our study presents a promising direction for effectively addressing the WiTA task and holds potential for further advancements in this domain.INDEX TERMS Air writing recognition, Transformer model, Human-computer interaction (HCI)

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“…It was shown that FCN provides high detection accuracy in addition to less processing times and fewer memory requirements than other NN. Region Proposed Networks (RPN) [100], [101] such as Faster R-CNN has been deployed in an array of object detection-related applications, for example, in accounting applications such as receipt information extraction [102], handwriting text recognition [103], and matching process-related applications [104]. It has also been applied in detecting mathematical expressions in scienti c document images [105] and tested in a plagiarism detection tool [106].…”

Section: Introductionmentioning

confidence: 99%

Enhanced safety implementation in 5S + 1 via object detection algorithms

Shahin

Chen

Hosseinzadeh

et al. 2023

Int J Adv Manuf Technol

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Scholarly work points to 5S+1, a simple yet powerful method of initiating quality in manufacturing, as one of the foundations of Lean manufacturing and the Toyota Production Systems. The 6 th S, safety, is often used to prevent future occupational hazards, therefore, reducing the loss of time, money, and human resources. This paper aims to show how Industry 4.0 technologies such as computer-based vision and object detection algorithms can help implement the 6 th S in 5S+1 through monitoring and detecting workers who fail to adhere to standard safety practices such as wearing Personal Protective Equipment (PPE).The paper evaluated and analyzed three different detection approaches and compared their performance metrics. In total, seven models were proposed to perform such a task. All the proposed models utilized You-Only-Look-Once (YOLO v7) architecture to verify workers' PPE compliance. In approach I, three models were used to detect workers, safety helmets and safety vests. Then, a machine learning algorithm was used to verify if each detected worker is in PPE compliance. In approach II, the model simultaneously detects individual workers and veri es PPE compliance. In approach III, three different models were used to detect workers in the input feed. Then, a deep learning algorithm was used to verify the safety. All models were trained on Pictor-v3 dataset. It is found that the third approach, when utilizing VGG-16 algorithm, achieves the best performance, i.e., 80% F1 score, and can process 11.79 Frames per Second (FPS), making it suitable for real-time detection.

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MC-OCR Challenge 2021: Deep Learning Approach for Vietnamese Receipts OCR

Cited by 17 publications

References 15 publications

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

An End-to-End Air Writing Recognition Method Based on Transformer

Enhanced safety implementation in 5S + 1 via object detection algorithms

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