A Survey on Text Information Extraction from Born-Digital and Scene Text Images

Joan, S. P. Faustina; Valli, S.

doi:10.1007/s40010-017-0478-y

Cited by 11 publications

(3 citation statements)

References 81 publications

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“…Remarkable progress has been made in the research of sub-tasks in the overall image scene understanding. If the overall image scene understanding can be organically integrated with tasks and features from the perspective of human scene understanding, it is more in line with human thinking mode, because people always have the awareness of overall understanding unconsciously when observing an image [7]. Meanwhile, it is more helpful to adopt the thinking mode of overall understanding.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Image Scene and Object Information Extraction based on Bayesian Network Model

Zhao

Guo²

2023

Applied Mathematics and Nonlinear Sciences

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In order to better extract scene and object information from computer image, a construction object extraction algorithm based on Bayesian network is proposed. The algorithm is trained by multi-scene aerial images to build a grain dictionary and map the grain in the actual image to the grain dictionary to obtain the scene information of the image;Then naive Bayesian networks were used to model the constraints of the relationship between architectural targets and the spatial context of scene classes, and the extraction of architectural targets was converted into a posteriori probability problem for solving Bayesian network class nodes. The experimental results show that the proposed algorithm can effectively extract architectural objects from aerial images. The experiment result shows that:In this paper, the proportion of target pixels accurately extracted by the algorithm is taken as the standard to define the standard of target pixels accurately extracted by the algorithm to reach more than 90% of the building target pixels. The average time of training an image is 2 s, which is mainly spent on the convolution operation with the filter. After the training, the average time of processing a single test image is 0.5s. It is proved that Bayesian network model can effectively extract scene and object information from computer image.

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Section: Literature Reviewmentioning

confidence: 99%

Computer Image Scene and Object Information Extraction based on Bayesian Network Model

Zhao

Guo²

2023

Applied Mathematics and Nonlinear Sciences

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“…For the problem that the image is too bright or too dark, the packaging bottle image is enhanced using histogram equalization; there is more or less noise in the image, and different filtering methods are used for different noises. is paper focuses on the detection of packaging bottles based on the AdaBoost algorithm [16,17]. We classify the test sample set by the clustering method in advance.…”

Section: Current Status Of Researchmentioning

confidence: 99%

A Study on the Design and Implementation of an Improved AdaBoost Optimization Mathematical Algorithm Based on Recognition of Packaging Bottles

Guo-zhu

Tsai

2022

Mathematical Problems in Engineering

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In this paper, a special design system is developed based on the design of the packaging bottle to achieve the effective acquisition of the image of the cross-section of the packaging bottle to be measured under the condition of limited space size, avoiding the distortion of the object to be measured. At the same time, the image of the area where the target packaging bottle is located is segmented, and the curve features are quickly determined and effectively matched with the template library to realize the recognition of the shape features of the bottle. In this paper, the design of the packaging bottle is first designed by mechanism design and 3D modeling, followed by rapid prototyping methods such as 3D printing, and the prototype is made for functional verification. Finally, the transmission speed and stability of the design system for packaging bottle recognition are improved through structural analysis and optimization methods. To realize the intelligent control of the packaging bottle transmission and identification system, the hardware control circuit is designed and the relevant intelligent control program is prepared based on the embedded system so that the packaging bottles in the transmission process can be quickly and accurately positioned and identified. An improved AdaBoost algorithm is proposed for packaging bottle detection. In the process of algorithm learning, the Haar features are too large and time-consuming, and the training sample is cropped to remove the sample edge pixels, which effectively reduces the number of features, thus reducing the computation. The proposed optical flow method is used to obtain the motion region in the video image as the region of interest, and the canny operator is used in the region of interest for edge detection, and the region of interest is filtered by the edge energy to exclude the noninterest region. Finally, the AdaBoost algorithm is used to detect the region of interest, which reduces the detection area and decreases the detection time. The improved AdaBoost algorithm has a high accuracy improvement over the traditional AdaBoost algorithm for the recognition of various packaging bottles with relatively suitable training set samples, and the system recognition time has reached the requirements of industrial recognition.

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“…The structure for mining images by concealing substance. Their structure gives the probability of using evaluation of resemblance among images and two sorts of quantization [7][8][9]. The procedure here presented ponders just recuperation points.…”

Section: Literature Surveymentioning

confidence: 99%

A Weight Based Feature Extraction Model on Multifaceted Multimedia Bigdata Using Convolutional Neural Network

Maddumala¹,

Arunkumar²

2020

ISI

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This paper intends to present main technique for feature extraction on multimeda getting well versed and a challenging task to handle big data. Analyzing and feature extracting valuable data from high dimensional dataset challenges the bounds of measurable methods and strategies. Conventional techniques in general have less performance while managing high dimensional datasets. Lower test size has consistently been an issue in measurable tests, which get bothered in high dimensional information due to more equivalent or higher component size than the quantity of tests. The intensity of any measurable test is legitimately relative to its capacity to lesser an invalid theory, and test size is a significant central factor in producing probabilities of errors for making substantial ends. Thus one of the effective methods for taking care of high dimensional datasets is by lessening its measurement through feature selection and extraction with the goal that substantial accurate data can be practically performed. Clustering is the act of finding hidden or comparable data in information. It is one of the most widely recognized techniques for realizing useful features where a weight is given to each feature without predefining the various classes. In any feature selection and extraction procedures, the three main considerations of concern are measurable exactness, model interpretability and computational multifaceted nature. For any classification model, it is important to ensure that the productivity of any of these three components isn't undermined. In this manuscript, a Weight Based Feature Extraction Model on Multifaceted Multimedia Big Data (WbFEM-MMB) is proposed which extracts useful features from videos. The feature extraction strategies utilize features from the discrete cosine methods and the features are extracted using a pre-prepared Convolutional Neural Network (CNN). The proposed method is compared with traditional methods and the results show that the proposed method exhibits better performance and accuracy in extracting features from multifaceted multimedia data.

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A Survey on Text Information Extraction from Born-Digital and Scene Text Images

Cited by 11 publications

References 81 publications

Computer Image Scene and Object Information Extraction based on Bayesian Network Model

Computer Image Scene and Object Information Extraction based on Bayesian Network Model

A Study on the Design and Implementation of an Improved AdaBoost Optimization Mathematical Algorithm Based on Recognition of Packaging Bottles

A Weight Based Feature Extraction Model on Multifaceted Multimedia Bigdata Using Convolutional Neural Network

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