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LTP based text detector for natural scene images
License: GNU General Public License v3.0
This project forked from mop/ltptextdetector
LTP based text detector for natural scene images
License: GNU General Public License v3.0
LTPTextDetector =========================================== This repository contains the open source release of the research publication End-to-End Text Recognition using Local Ternary Patterns, MSER and Deep Convolutional Neural Networks Michael Opitz, Markus Diem, Markus Diem, Florian Kleber, Stefan Fiel and Robert Sablatnig presented at DAS 2014. Requirements =========================================== * Linux (untested under Windows, OS X) * Boost * OpenCV 2.4 * CMake * Eigen 3 * python 2.7 * LTPTextDetectorTraining (available on github) * Time and patience to get things running How to compile the code? =========================================== Before compiling the code, grab the LTPTextDetectorTraining project on GitHub and extract/symlink it in the detector subdirectory. Then the project can be compiled by $ cmake . && make Since GitHub does not allow big files in their repositories, pre-trained models have to be downloaded at http://bit.ly/1ehC3ZT and unzipped in the models/ directory How to run a demo? =========================================== Just run $ ./bin/demo -c config_11.yml -model models/model_boost.txt -i <image> from the root-directory of the project. The model files must be downloaded and extracted in the models directory, as explained in the previous step. How to reproduce the results? =========================================== To reproduce the results, download the archive of datasets from http://bit.ly/1gxI9Fx and unzip it in the parent directory of the project. Then run $ ./bin/classify -t ../test_icdar_2011 -r ../result_test -m models/model_boost.txt to create the response maps and $ ./bin/create_boxes -c config_11.yml to create the bounding boxes. To convert the output to the ICDAR evalution format, run $ python2 ./scripts/to_xml.py result_test/ > eval11.xml $ evaldetection eval11.xml datasets/test-textloc-gt/test-gt-textloc-wolf.xml > results.xml $ readdeteval results.xml Which shoult print: Included 255 images with non-zero groundtruth Included 0 images with zero groundtruth Skipped 0 images with zero groundtruth. Total-Number-Of-Processed-Images: 255 100% of the images contain objects. Generality: 4.66275 Inverse-Generality: 0.214466 <evaluation noImages="255"> <icdar2003 r="0.700094" p="0.81904" hmean="0.75491" noGT="1189" noD="1026"/> <score r="0.715559" p="0.844055" hmean="0.774514" noGT="1189" noD="1026"/> </evaluation> How to retrain the models? =========================================== Training scripts are in the scripts/ subdirectory. To retrain the models unzip the datasets in the parent directory of the repository. To retrain everything from scratch run $ ./script/train_all.sh What about the Recognizer? =========================================== Comming soon...
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