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Merging method of power signal model training files

A model training, power signal technology, applied in file systems, file/folder operations, computer parts, etc., can solve problems such as low recognition efficiency and long processing time

Pending Publication Date: 2021-04-20
南京太司德智能电气有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since the training needs to adjust the position of the text in the picture and the size of the text box a lot, it will bring a huge workload to the text training. If the training files are not merged, the processing time will be long and the recognition efficiency will be low.

Method used

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  • Merging method of power signal model training files
  • Merging method of power signal model training files
  • Merging method of power signal model training files

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] Embodiment 1: as figure 1 As shown, an efficient model training method includes the following steps:

[0039] 1) Form the names of stations and status names in the power dispatching master station system into files in txt format or execl format, and read the txt or excel files; due to the large number of Chinese characters, this step will carry out the text that needs to be recognized in practical applications Sorting, on the one hand, reduces the number of characters to be recognized, and on the other hand, reduces the size of the generated model, which can improve the speed of character recognition;

[0040] 2) Set the font, size, and model name parameters of the training text to improve the accuracy of text recognition;

[0041] 3) Read the selected txt or excel file by line, and obtain the total number of lines of text (denoted as num_lines) and the number of words in the line with the largest number of words in these lines (denoted as max_length); this step is mai...

Embodiment 2

[0092] Embodiment 2: as figure 2 Shown, a kind of merging method of power signal model training file, this method comprises the following steps:

[0093] 1) selection has been manually marked and has generated a .box text file and a picture file with a suffix of .tif, which can be a .box text file and a suffix of the picture file of .tif generated by steps 1)-10) in embodiment 1, It can also be adjusted by other methods or tools; image files in .tif format under different paths can be selected, which is more convenient than the traditional method of manually copying the merged files to the same path;

[0094] 2) Set the training parameters of each file, including training language and page mode parameters, the default is Chinese training language; different parameters can be set for each selected file, compared with the traditional method, it is necessary to enter parameters and execute commands separately for each file The method is more intuitive and convenient;

[0095] ...

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Abstract

The invention discloses a merging method of power signal model training files. The method comprises the following steps: selecting. Box text files and picture files with suffixes. Tif; setting file parameters; automatically naming the picture file with the tif suffix and the box file to form a training file conforming to the specification; writing the file name in the set format into a text file of the font-properties; calling a tesseract command to generate a text file of which the suffix is. Tr for each file of which the suffix is. Tif; respectively combining all the text file names with suffixes of. Box and all the text file names with suffixes of. Tr into character strings separated according to spaces; and finally, calling a combine-tessdata command of the tessract, and generating a. Traineddata file. Through the multi-model merging method, the manually marked character pictures in the actual application and the character pictures generated by the method are merged, and the recognized wrong characters are adjusted through the manually marked data in the actual application, so that the training workload is reduced, and meanwhile, the character recognition accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of Chinese training model training methods, and in particular relates to a method for merging training files of electric signal models. Background technique [0002] The recognition rate of the Chinese training model that comes with the Tesseract text recognition engine is low. It is a common practice for users to improve the text recognition rate by retraining users' commonly used characters. Since the training needs to adjust the position of the text in the picture and the size of the text box a lot, it brings a huge workload to the text training. If the training files are not merged, the processing time will be long and the recognition efficiency will be low. Contents of the invention [0003] The technical problem to be solved by the present invention is to provide a method for merging power signal model training files to solve the problems existing in the prior art. [0004] The technical solution ad...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F16/16G06F16/50
Inventor 张海永高承贵
Owner 南京太司德智能电气有限公司
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