Japanese Scene Character Recognition using Random Image Feature and Ensemble Scheme

At present, there are still many deficiencies in Chinese-Japanese machine translation methods, the processing of corpus information is not deep enough, and the translation process lacks rich language knowledge support. In particular, the recognition accuracy of Japanese characters is not high. Based on machine learning technology, this study combines image feature retrieval technology to construct a Japanese character recognition model and uses Japanese character features as the algorithm recognition object. Moreover, this study expands image features by generating a brightness enhancement function using a bilateral grid. In order to exclude the influence of the edge and contour of the image scene on the analysis of the image source, the brightness value of the HDR image is used instead of the pixel value of the image as the image data. In addition, this research designs experiments to study the translation effects of this research model. The research results show that the model proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

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A Study on Various Image Feature Extraction Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1195528 ◽

2019 ◽

pp. 151-157

Author(s):

Merllin Ann George ◽

L. C. Manikandan

Keyword(s):

Image Processing ◽

Feature Extraction ◽

Character Recognition ◽

Quantitative Information ◽

Image Feature ◽

Extraction Techniques ◽

Unique Form ◽

Image Feature Extraction ◽

Time Consumption ◽

Matrix Vector

Feature Extraction is the technique of extracting quantitative information from a image. Feature plays a very important role in the area of image processing. The purpose of feature extraction technique in image processing is to represent the image in its compact and unique form of single values or matrix vector. Feature extraction techniques are helpful in various image processing applications e.g. character recognition. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. The aim of this paper is to give the overview of image feature extraction techniques for young learners and researchers.

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High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100143663 ◽

1986 ◽

Vol 44 ◽

pp. 414-415

Author(s):

W. Krakow ◽

D. A. Smith

Keyword(s):

Atomic Structure ◽

High Resolution Electron Microscopy ◽

Image Features ◽

Image Feature ◽

Resolution Electron ◽

Tilt Boundaries ◽

Processing Techniques ◽

Atom Position ◽

Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

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SELECTION TECHNIQUE FOR MULTIPLE OUTPUTS OF OPTICAL CHARACTER RECOGNITION

Eurasian Journal of Mathematical and Computer Applications ◽

10.32523/2306-6172-2020-8-2-41-51 ◽

2020 ◽

Vol 8 (2) ◽

pp. 41-51

Author(s):

I.Q. Habeeb ◽

Z.Q. Al-Zaydi ◽

H.N. Abdulkhudhur

Keyword(s):

Character Recognition ◽

Optical Character Recognition ◽

Selection Technique ◽

Multiple Outputs ◽

Optical Character

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A Structured Method for the Recognition of Complex Historical Tables

History and Computing ◽

10.3366/hac.1997.9.1-3.58 ◽

1997 ◽

Vol 9 (1-3) ◽

pp. 58-77

Author(s):

Vitaly Kliatskine ◽

Eugene Shchepin ◽

Gunnar Thorvaldsen ◽

Konstantin Zingerman ◽

Valery Lazarev

Keyword(s):

Nineteenth Century ◽

Character Recognition ◽

Optical Character Recognition ◽

Complex Structure ◽

Source Material ◽

Historical Sources ◽

Tax Assessment ◽

Optical Character ◽

Algorithmic Model ◽

Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

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