Optical character recognition method in patent text scene

An optical character recognition and text recognition technology, applied in the field of optical character recognition, can solve the problems of spending a lot of time, decreased accuracy, poor effect, etc., to achieve the effect of improved recognition effect, good practical value and research significance, and broad application prospects.

Pending Publication Date: 2020-01-10
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The main disadvantages are: (1) It takes a lot of time to do feature extraction. Usually, artificially designed features (such as histograms of directional gradients, etc.) are used to train character recognition models. Such single features can generalize quickly when fonts change. decline
(2) Excessive reliance on the result of character segmentation, in th

Method used

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  • Optical character recognition method in patent text scene
  • Optical character recognition method in patent text scene
  • Optical character recognition method in patent text scene

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Example Embodiment

[0051] The practicability of the present invention will be explained below with a simulation example.

[0052] Define the training environment:

[0053] CPU-i7 8700k, GPU NVIDIA GeForce 2080Ti, OS ubuntu 16.0.4.

[0054] Data verification environment:

[0055] CPU 2.7GHz Intel Core i5, GPU Intel Iris Graphics 6100, Mac OS X 10.14.6.

[0056] The development language uses python3.5, the open source framework Keras, Tensorflow is used as the backend, and third-party libraries such as Opencv and Numpy are introduced.

[0057] 1. Data set preparation

[0058] Patented text images in tif format. The data set includes 500,000 original images, including Chinese, English, numbers, and punctuation. At the same time, data is enhanced through image processing methods such as stretching, blurring, random cropping, perspective transformation, and inverted color. , The final data set has a total of about 3 million images. The data set is divided into training set and validation set according to the r...

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Abstract

The invention belongs to the technical field of computer vision, image processing and convolutional neural networks, and particularly relates to an optical character recognition method in a patent text scene. According to the method, a CNN and an LSTM are combined, the advantages of the CNN and the LSTM are achieved at the same time, the problem that the CNN is weak in sequence correlation processing is solved, and the defect that the LSTM is insufficient in image feature extraction is overcome. According to the present invention, a new loss function calculation method CTC is combined to solvethe problem that the sample data is difficult to align during the text recognition process in a manner without alignment.

Description

technical field [0001] The invention belongs to the technical fields of computer vision, image processing and convolutional neural network, and in particular relates to an optical character recognition method in a patent text scene. Background technique [0002] With the continuous updating of computer hardware and software, and the gradual maturity of artificial intelligence (AI), it is of great practical significance to apply deep learning to the field of optical character recognition. Optical character recognition is to convert the text of various bills, newspapers, books, manuscripts and other printed materials into image information through optical input methods such as scanning, and then use text recognition technology to convert the image information into information that can be recognized by computers. Because there are too many influencing factors, including the habit of the writer, the printing quality of the document, the scanning quality of the scanner, the recog...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V30/413G06V30/40G06V30/153G06N3/045G06F18/241
Inventor 饶云波郭毅程亦茗张孟涵王艺霖
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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