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Medical bill rotation correction method and system based on deep learning

A technology of rotation correction and deep learning, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as inability to calculate angles correctly, achieve fast and efficient rotation correction, overcome large angle inclination, and meet classification speed requirements Effect

Pending Publication Date: 2020-11-03
晶璞(上海)人工智能科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, bill tilt correction adopts the method of straight line detection and calculation of arc angle. The main defect is that the angle cannot be calculated correctly when medical bills are irregular.

Method used

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  • Medical bill rotation correction method and system based on deep learning
  • Medical bill rotation correction method and system based on deep learning

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Embodiment Construction

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on The embodiments of the present invention and all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0056] see Figure 1-2 , the present invention provides a technical solution:

[0057] Such as figure 1 As shown, the rotation correction method of the medical bill provided in this embodiment includes:

[0058] Step 101: Manually mark the receipt image, and perform image enhancement processing on the classified images to obtain sample image data;

[0059] The bill image in this embodiment can be the image data obtained by photographing with ...

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Abstract

The invention relates to the technical field of image classification in deep learning, in particular to a medical bill rotation correction method and system based on deep learning, and the method comprises the following specific steps: carrying out the manual marking of a bill image, carrying out the image enhancement of a classified image, and obtaining sample image data; dividing the sample image data into a training set, a test set and a verification set; constructing a deep neural network framework, loading a neural network model, and setting hyper-parameters to obtain an initial bill rotation correction model; training the initial bill rotation correction model by using the training set, and testing and verifying the trained initial bill rotation correction model by using the test setand the verification set to obtain an optimal bill rotation correction model; according to the medical bill rotation correction method and system, the situations of inclination, bending and the likeof the medical bill are effectively overcome through the design, and recognition of the bill from the back side is facilitated.

Description

technical field [0001] The invention relates to the technical field of image classification in deep learning, in particular to a method and system for correcting rotation of medical bills based on deep learning. Background technique [0002] In recent years, with the popularization of data imaging equipment and the rapid development of deep learning technologies such as convolutional neural network (CNN) in the field of image classification, text recognition software continues to emerge, such as Huawei cloud recognition, Tencent cloud recognition, etc., text recognition accuracy constantly improving. But these are all general text recognition. For the specific field of medical bill recognition, it is necessary to correct the skewed bills first, so that the key information can be sliced ​​and recognized later. At present, bill tilt correction adopts the method of straight line detection and calculation of arc angle. The main defect is that the angle cannot be calculated corr...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/08
CPCG06N3/08G06V30/40G06V30/1478G06V30/10G06F18/214
Inventor 周审章严京旗陈俊霞李进文卞志强张成栋
Owner 晶璞(上海)人工智能科技有限公司
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