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Related Concept Videos

Three-Winding Transformers01:19

Three-Winding Transformers

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Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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A visual transformer-based smart textual extraction method for financial invoices.

Tao Wang1, Min Qiu2

  • 1School of Innovation and Entrepreneurship, Zhengzhou University of Science and Technology, Zhengzhou 450064, China.

Mathematical Biosciences and Engineering : MBE
|December 5, 2023
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Summary

This study introduces a deep learning model using a visual transformer for financial invoice text extraction. The approach enhances accuracy and robustness in extracting key information from invoices.

Keywords:
computer visionsmart recognitiontextual extractionvisual transformer

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Natural Language Processing

Background:

  • Traditional financial invoice text extraction relies on manual feature engineering, limiting applicability and robustness.
  • Existing methods struggle with diverse invoice layouts and require scene-specific development.
  • The era of big data necessitates more adaptive and generalized solutions for automated data extraction.

Purpose of the Study:

  • To develop a lightweight and robust deep learning model for computer vision-assisted textual extraction from financial invoices.
  • To leverage the adaptive feature learning capabilities of deep learning to overcome limitations of traditional methods.
  • To introduce a novel approach using a visual transformer for improved financial invoice information extraction.

Main Methods:

  • Preprocessing financial invoice images using image processing techniques.
  • Employing a sequence transduction model with a visual transformer architecture for information extraction.
  • Utilizing horizontal-vertical projection for character segmentation and template matching for character normalization.
  • Applying a multi-head attention mechanism within the transformer structure for fine-grained feature relationship capture.
  • Implementing a text classification procedure for outputting detection results.

Main Results:

  • The proposed visual transformer-based model demonstrated superior performance compared to traditional methods.
  • Experimental results on a real-world dataset confirmed high accuracy in extracting financial invoice information.
  • The method exhibited significant robustness across various financial invoice scenarios.

Conclusions:

  • Deep learning, specifically the visual transformer, offers a powerful solution for enhancing financial invoice text extraction.
  • The developed model provides a more generalizable and robust alternative to traditional image processing techniques.
  • This approach significantly improves the efficiency and reliability of automated financial data extraction.