Automatic character recognition for moving and stationary vehicles and containers in real-life images

Lee, J.C.M.

doi:10.1109/ijcnn.1999.833530

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…The authors used simulation to test the decision policies and compared with actual experiences. (Lee, 1999) contributed with a successfully implemented automatic character recognition system for identification of vehicle and container numbers.…”

Section: Artificial Intelligencementioning

confidence: 99%

A multi-agent system with blockchain for container stacking and dispatching

Henesey¹,

Yulia²,

Anwar³

2019

Proceedings of the 21st International Conference on Harbor, Maritime and Multimodal Logistic Modeling &Amp; Simulation (HMS 201

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Port Logistical Supply chains play a very important role in society. Their complex and adaptive behaviours promote the suggested applications of combining a multiagent system with blockchain for solving complex problems. Several technologies have been proven positively to work in logistics, however the concept of combining converging technologies such as blockchain with deep reinforcement multi agent is viewed as a novel approach to solving the complexity that is associated with many facets of logistics. A simulator was developed and tested for the problem of container stacking. The simulation results indicate a more robust approach to currently used tools and methods.

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Section: Artificial Intelligencementioning

confidence: 99%

A multi-agent system with blockchain for container stacking and dispatching

Henesey¹,

Yulia²,

Anwar³

2019

Proceedings of the 21st International Conference on Harbor, Maritime and Multimodal Logistic Modeling &Amp; Simulation (HMS 201

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“…But as the character size is quite different, the shape and size of the morphology Structuring Element (SE) is difficult to be decided. An extraction algorithm through gray level quantization, thresholding and stroke analysis was also proposed by Lee [11,12]. But quantization may give different pixels different gray level in the same character when the gray level is nearby the quantization threshold, and this will result in many broken strokes.…”

Section: Related Workmentioning

confidence: 99%

Container Code Detection by Deep Convolutional Network

Wang¹,

Ma²

2017

Proceedings of the 2017 Global Conference on Mechanics and Civil Engineering (GCMCE 2017)

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Abstract. Automatic container code recognition plays an important role in customs logistics and tra-nsport management. Due to difficulties such as character color and font size variation, illumination conditions, image degradation, and exist of many other characters, automatic detect and recognition of container code is still a difficult task. This paper proposes a container code detection algorithm based on deep convolutional neural network named holistically-nested edge detection (HED). In the training phase, a bounding box was drawn around container code as virtual edges, and they were feed to the network together with original image to train the HED model. In the test phase, probability map of bounding box was predict by trained model and finally bounding box is obtained by thresholding and connected region analysis. Experimental results on 9953 container images show that the performance of IOU and recall precision on test set can reach 0.646 and 0.934 respectively with the proposed algorithms.

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“…Due to the incurred problems related to rotated images taken from real-life scenarios, many papers tackled this issue in order to detect and rotate the skewed images. The existing algorithms consist of rotating the image after calculating its inclination angle [1][2][3] and before proceeding into the next steps of the Optical Character Recognition (OCR) process.…”

Section: Introductionmentioning

confidence: 99%

Reading Skewed Images without Image Rotation

Al-Khawand

2014

BJMCS

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Aims:To extract characters from skewed images without image rotation. Study Design: This study is designed to be implemented at the gates of Customs, Port Authorities, Terminal Operators and it can also be implemented for vehicles traffic management. Place and Duration of Study: Lebanon, between September and November 2013. Methodology: The proposed method consists of sorting the segmented characters according to the X axis, then assigning a Program Line Number to each character based on the skew angle and finally sorting the Program Line Numbers according to their intersection with the Y axis. Results: Our approach is capable of handling any font and size of characters and it is robust and efficient; regarding its complexity for an image having N lines and M characters, the worst CPU time usage and the worst memory usage is equal to O (NxM) while the network usage and disk usage for one image is O (1) which led to a 0.11 milliseconds response time to extract all container number digits. Conclusion: Acceleration of segments' extraction from skewed images by avoiding image rotation in order to acquire a faster and more accurate OCR process.

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Automatic character recognition for moving and stationary vehicles and containers in real-life images

Cited by 15 publications

References 5 publications

A multi-agent system with blockchain for container stacking and dispatching

A multi-agent system with blockchain for container stacking and dispatching

Container Code Detection by Deep Convolutional Network

Reading Skewed Images without Image Rotation

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