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开源软件名称:JarveeLee/SynthText_Chinese_version开源软件地址:https://github.com/JarveeLee/SynthText_Chinese_version开源编程语言:C++ 92.8%开源软件介绍:SynthText from AnkushI can no longer maintain this OCR and SynthText project because I change my research domain according to the requirement of my new affiliation.....Anyone want to hold this can tell me , I can give the master to you as long as you can really understand the code in details. Modify from https://github.com/ankush-me/SynthText.git to generate chinese character My OS is Ubuntu opencv2.4 But I am not sure whether it can run on other OS I changed some func,just run gen.py will be OK,in gen.py I change the depth prediction map with gray map for generating char on cartoon image , for natural img you need to change back to depth map ,other gen**.py contains similar code with different path I do for myself... 0,Before running this code make sure your OS support unicode for chinese.. which as well cost me hours....Added chinese may not make sense because in English words are saperated by blank meanwhile in chinese words are saperated by meaning. 1,In synthGen I added a function called is_chinese(char ) to or with is_english to cal num of valid chars. 2,Updated the .tff char style files and the path.txt,then 3,some utf-8 decoded and encoded for chinese char ....Ah I forgot the details.... 4,So you can add more pic into the dataset and check with issue under the anthor to fix mistakes...... 5,If you want to add more img , firstly you need to compute the segmentation and depth prediction by the 2 matlab code and 1 python code provided by author, and then use the add_more_data.py to generate a new big dset.h5 , containing all of imgs and their seg and depth, then rerun gen.py to see its performance. These are some samples I do. ** Synthetic Scene-Text Image Samples** Code for generating synthetic text images as described in "Synthetic Data for Text Localisation in Natural Images", Ankush Gupta, Andrea Vedaldi, Andrew Zisserman, CVPR 2016. ** Synthetic Scene-Text Image Samples** The library is written in Python. The main dependencies are:
Generating samples
This will download a data file (~56M) to the
This script will generate random scene-text image samples and store them in an h5 file in
Pre-generated DatasetA dataset with approximately 800000 synthetic scene-text images generated with this code can be found here. [update] Adding New ImagesSegmentation and depth-maps are required to use new images as background. Sample scripts for obtaining these are available here.
For an explanation of the fields in Further InformationPlease refer to the paper for more information, or contact me (email address in the paper). |
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