The article presents a method of recognizing alphanumeric characters located in the image, based on a previously created database of patterns using neural networks. For this purpose the convolutional networks were use...
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ISBN:
(纸本)9781510636729
The article presents a method of recognizing alphanumeric characters located in the image, based on a previously created database of patterns using neural networks. For this purpose the convolutional networks were used, which independently search for features that allow to distinguish characters in the image. A larger number of convolution layers allows us to recognize a greater number of features and thus to increase the probability of correctly recognized characters. The main purpose of the paper is to present software that recognizes the alphanumeric characters in images and to investigate the impact of the size of this database on the program's speed and character recognition efficiency. This software can also be used in more complex structures, such as automatic translators or as a computer reader. The calculation of the first program that recognizes single character and the second program that reads all the text from the image have been made in the MATLAB environment. The paper describes the components of this software, such as the learning subsystem and the character recognition subsystem. The results of the program were presented in the form of screenshots showing the results of the learning process and character recognition process. The speed of the software and the effectiveness of recognizing alphanumeric characters using the artificial neural networks and maximally stable extremal regions (mser) algorithm are presented in the table and figures. Attention was also paid to the impact of the size of the database used to learn the network on the speed of calculations and recognition efficiency.
The mser algorithm for determining the warm-up period in steady-state simulations was introduced in 1990. Over the past two decades, empirical evaluations by different research teams have demonstrated the effectivenes...
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ISBN:
(纸本)9781424498642
The mser algorithm for determining the warm-up period in steady-state simulations was introduced in 1990. Over the past two decades, empirical evaluations by different research teams have demonstrated the effectiveness of the procedure. These extensive empirical results illustrate the relative advantage of mser over other approaches under alternative bias scenarios. In this paper, we develop parametric expressions to explore how mser should behave in the case of simulation output with geometrically decaying bias and white noise. We derive a closed-form expression for the expected optimal truncation point for this scenario. This permits computation of the threshold bias-to-noise ratio for bias detection in terms of the rate of decay of the initial bias and the strength of autocorrelation in the output sequence.
Port container handling is an important part of terminal operations. When loading multiple containers, operators can only rely on human eyes and PLC information to obtain the location of the container. To avoid the se...
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ISBN:
(纸本)9781728139364
Port container handling is an important part of terminal operations. When loading multiple containers, operators can only rely on human eyes and PLC information to obtain the location of the container. To avoid the serious consequences of miscalculation, a combination algorithm is proposed in this paper. We choose coastline, container ridgeline and container number as features to analyses and locate. coastline location algorithm based on color space and improved OTSU segmentation. Container ridgeline location based on Sobel and clustering algorithm. Container number location based on Maximally Stable Extremal Regions (mser) algorithm. After testing 71 group operation videos of 4 bridge cranes in Ningbo port, the combination algorithm achieves better positioning accuracy. The average processing time of a single frame is less than 0.6s. The algorithm proposed in this paper provides an effective reference and solution for improving the efficiency of terminal operations.
In this paper, we propose a new method for natural image text detection under a weakly supervised data set. Currently, most of the text detection models are based on bounding box label training data. However, the cost...
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ISBN:
(纸本)9783030341206;9783030341190
In this paper, we propose a new method for natural image text detection under a weakly supervised data set. Currently, most of the text detection models are based on bounding box label training data. However, the cost of the bounding box label training data is very high. In order to solve this problem, we propose an attention mechanism that can be trained on image-level labels data and roughly identifies text regions via an automatically learned attentional map based on a convolutional neural network. There are three main steps: firstly, a VGG model is trained using image-level labels data to score the likelihood that a text region exists in the picture;secondly, the region of interest is extracted by means of the attention mechanism and the extracted region is evaluated using the network trained in the first step to getting the text region and finally, the text line is extracted in the text region using the mser algorithm. Trained with the weakly supervised data which is only with image-level labels, our model can generate bounding boxes for the text line in the image. The results of our model are very close to those of the models using bounding box label training data on the text detection benchmark sets of MSRA-TD500, ICDAR2013, and ICDAR2015.
In order to solve the problem that traditional Camshift algorithm can easily fail to track overlapping targets and multiple similar depth targets, a new improved maximally stable extremal regions (mser) algorithm is p...
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ISBN:
(纸本)9789811323843;9789811323836
In order to solve the problem that traditional Camshift algorithm can easily fail to track overlapping targets and multiple similar depth targets, a new improved maximally stable extremal regions (mser) algorithm is presented in this paper. Firstly, the suspected target contour is extracted and similarity analysis is performed. Secondly, the improved mser algorithm is used to confirm the target contour and update the similarity library. Finally, combined with the physical properties unique to the depth image and based on the Kalman filter, it is possible to predict the tracking target's moving position. The experimental results show that the real-time performance and recognition rate are improved, and robustness to the situation of target overlap and occlusion is better with the improved mser algorithm.
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