Custom C++ implementation of EasyOCR
This C++ project implements the pre/post processing to run a OCR pipeline consisting of a text detector CRAFT, and a CRNN based text recognizer. Unlike the EasyOCR python which is API based, this repo provides a set of classes to show how you can integrate OCR in any C++ program for maximum flexibility. The torchExample.cpp main program highlights how to utilize all elements of the EasyOCR-cpp pipeline. Because a test program is only provided, make sure to configure your input image within torchExample.cpp if you only plan to utilize the test program.
thrown together quickly within a week so many debug comments :)
Libtorch is being utilized with an in-house class I usually use for C++ inference TorchModel, and OpenCV for the pre/post processing steps. The TorchModel class can easily adapted to run inference on most Pytorch models converted to TorchScript. Provides some handy functions to pre process opencv::Mat and handle device usage (GPU,CPU). Great starting point for C++ based inference for Pytorch Models.
Some features that have yet to be implemented:
- beam search, only implemented greedy decoding
- .txt/.pdf output
- exact bounding box merge alg from EasyOCR, opted for custom one which is less complex
- support for other languages, atm only english is supported.
Setup with Docker (Thanks to @BBO-repo )
To build and run through visual studio code, make sure the remote development extension is installed.
The repository provides a .devcontainer.json which should allow you to directly reopen the cloned folder in a docker container using the bottom left icon and select Reopen in containeras illustrated below
When the container is finally built, you can open a terminal and your working folder should be /workspace/ocr-c++ as mentionned .devcontainer.json the field "workspaceFolder": "/workspaces/ocr-c++". You should have the two folders:
thirdpartycontaining the automatically downloaded libtorch libraryEasyOCR-cppcontaining the code
You can proceed as usual to build the cmake project
mkdir -p /workspaces/ocr-c++/EasyOCR-cpp/build
cd /workspaces/ocr-c++/EasyOCR-cpp/build
cmake ..
make
The binaries should be available in build folder, to run the example application just run:
cd /workspaces/ocr-c++/EasyOCR-cpp/build
./torchTest
Considering that the repository was cloned in the folder /my/working/directory/EasyOCR-cpp for illustration.
Build a docker image named for example ocr_engine from the Dockerfile inside the /my/working/directory/EasyOCR-cpp directory.
docker build --progress=plain -t ocr_engine /my/working/directory/EasyOCR-cpp
Build a container named for example EasyOCR-cpp from the ocr_engine generated image
docker create --name EasyOCR-cpp --mount type=bind,source=/my/working/directory/EasyOCR-cpp,target=/workspaces/ocr-c++/EasyOCR-cpp ocr_engine:latest
You can now start, stop or restart the generated EasyOCR-cpp container.
From a terminal, enter the following command to start the container and getting a terminal inside the container with an interactive mode
docker container start EasyOCR-cpp
docker exec -ti EasyOCR-cpp bash
You should have a bash terminal inside the container, entering pwd and ls commands should output the following:
pwd
/workspaces/ocr-c++
ls
thirdparty EasyOCR-cpp
Similarly to previously to build cmake project, go to source code folder cd /workspaces/ocr-c++/EasyOCR-cpp, make a folder build and go inside, then cmake .. and make
cd /workspaces/ocr-c++/EasyOCR-cpp
mkdir build
cd build
cmake ..
make
Then similarly, the binaries should be available in build folder, to run the example application just run:
cd /workspaces/ocr-c++/EasyOCR-cpp/build
./torchTest
Click to Download libtorch - > download
Use OpenCV Windows installer and unzip (v4.6) - > OpenCV libs
Make sure to change the location in the Makefile for OpenCV to point to your OpenCV build dir
Create a build directory within the repo, cd to it and run
cmake -DCMAKE_PREFIX_PATH=<absolute-path-to-libtorch-folder> ..
This will generate a solution within the build folder you can open up in Visual Studio. Make sure to use the Release config when building
Configure your input image here. Currently the test program is using the test.jpg which comes in the repo.
Launch from command-line, or within Visual Studio after building.
Since its designed to be used in a C++ program, text is not being written to disk at the moment An output image will be generated in the main repo dir containing an annotated version of the input image with detection bounding boxes
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