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YOLOv3-Tesseract Model for Improved Intelligent form Recognition
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Background:
YOLOv3-Tesseract is widely used for the intelligent form recognition because
it exhibits several attractive properties. It is important to improve the accuracy and efficiency
of the optical character recognition.
Methods:
The YOLOv3 exhibits the classification advantages for the object detection. Tesseract can
effectively recognize regular characters in the field of the optical character recognition. In this study,
a YOLOv3 and Tesseract-based model of improved intelligent form recognition is proposed.
Results:
First, YOLOv3 is trained to detect the position of the text in the table and to subsequently
segment text blocks. Second, Tesseract is used to individually detect text blocks and combine
YOLOv3 and Tesseract to achieve the goal of table character recognition.
Conclusion:
Based on the Tianchi big data, experimental simulation is used to demonstrate the proposed
method. The YOLOv3-Tesseract model is trained and tested to effectively accomplish the
recognition task.
Bentham Science Publishers Ltd.
Title: YOLOv3-Tesseract Model for Improved Intelligent form Recognition
Description:
Background:
YOLOv3-Tesseract is widely used for the intelligent form recognition because
it exhibits several attractive properties.
It is important to improve the accuracy and efficiency
of the optical character recognition.
Methods:
The YOLOv3 exhibits the classification advantages for the object detection.
Tesseract can
effectively recognize regular characters in the field of the optical character recognition.
In this study,
a YOLOv3 and Tesseract-based model of improved intelligent form recognition is proposed.
Results:
First, YOLOv3 is trained to detect the position of the text in the table and to subsequently
segment text blocks.
Second, Tesseract is used to individually detect text blocks and combine
YOLOv3 and Tesseract to achieve the goal of table character recognition.
Conclusion:
Based on the Tianchi big data, experimental simulation is used to demonstrate the proposed
method.
The YOLOv3-Tesseract model is trained and tested to effectively accomplish the
recognition task.
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