Developing an Algorithm for Sequential Sorting of Discrete and Connected Characters Using Image Processing of Multi-Line License Plates

Ahmed, Abu Jar Md. Minhuz Uddin; Uddin, Md. Anwar; Rahman, Md. Asadur

doi:10.1016/j.array.2021.100063

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…The sorting system is also used for sorting lima beans or sorting the connected characters of multiline license plates [ 15 , 16 ]. The automated sorting system based on vision detection for fruit functions is based on the detection of the size and the shape of fruit in real-time [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Developed an Automated Design Sorting System According to Outer Edges of Object

Hamzah

Abbas²

2022

The Scientific World Journal

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The automated sorting systems are used in the industrial sectors to increase the rate of production. This research developed the sorting system by using a vision machine to detect the matching of capturing image with the storage base image. The system will be matching and sorting in real-time with 5 cm/s conveyor belt speed. The vision system is based on three stages to arrive at the sorting decision. The first stage is to covert the capturing image to a binary image, the second stage is applying edge detection of the product, and the third stage is matching this result with the base image. The system was successful to sort any product with complexity in shape and with high efficiency. The system sorting can be detected and sorted any product/machine element at any position or orientation. The system uses real-time analysis in order to provide the required results. The results arrived at the sorting gate at the end conveyer belt of the system if open that means the product matching. Three different products were selected in order to investigate the response and the accuracy of the results. It was found that the maximum of error to detect the product is not exceeding 2% for all cases.

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Section: Introductionmentioning

confidence: 99%

Developed an Automated Design Sorting System According to Outer Edges of Object

Hamzah

Abbas²

2022

The Scientific World Journal

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ResneSt-Transformer: Joint attention segmentation-free for end-to-end handwriting paragraph recognition model

Hamdan

Cheriet

2023

Array

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Multiple attention-based encoder–decoder networks for gas meter character recognition

Wang

Ullah

et al. 2022

Sci Rep

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Factories swiftly and precisely grasp the real-time data of the production instrumentation, which is the foundation for the development and progress of industrial intelligence in industrial production. Weather, light, angle, and other unknown circumstances, on the other hand, impair the image quality of meter dials in natural environments, resulting in poor dial image quality. The remote meter reading system has trouble recognizing dial pictures in extreme settings, challenging it to meet industrial production demands. This paper provides multiple attention and encoder–decoder-based gas meter recognition networks (MAEDR) for this problem. First, from the acquired dial photos, the dial images with extreme conditions such as overexposure, artifacts, blurring, incomplete display of characters, and occlusion are chosen to generate the gas meter dataset. Then, a new character recognition network is proposed utilizing multiple attention and an encoder–decoder structure. Convolutional neural networks (CNN) extract visual features from dial images, encode visual features employing multi-head self-attention and position information, and facilitate feature alignment using the connectionist temporal classification (CTC) method. A novel two-step attention decoder is presented to improve the accuracy of recognition results. convolutional block attention module (CBAM) reweights the visual features from the CNN and the semantic features computed by the encoder to improve model performance; long short-term memory attention (LSTM attention) focuses on the relationship between feature sequences. According to experimental data, our system can effectively and efficiently identify industrial gas meter picture digits with 91.1% identification accuracy, faster inference speed, and higher accuracy than standard algorithms. The accuracy and practicality of the recognition can fulfill the needs of instrument data detection and recognition in industrial production, and it has a wide range of applications.

show abstract

Developing an Algorithm for Sequential Sorting of Discrete and Connected Characters Using Image Processing of Multi-Line License Plates

Cited by 4 publications

References 6 publications

Developed an Automated Design Sorting System According to Outer Edges of Object

Developed an Automated Design Sorting System According to Outer Edges of Object

ResneSt-Transformer: Joint attention segmentation-free for end-to-end handwriting paragraph recognition model

Multiple attention-based encoder–decoder networks for gas meter character recognition

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