opencv_contrib/modules/text

Scene Text Detection and Recognition in Natural Scene Images

The module contains algorithms to detect text, segment words and recognise the text. It's mainly intended for the "text in the wild", i.e. short phrases and separate words that occur on navigation signs and such. It's not an OCR tool for scanned documents, do not treat it as such. The detection part can in theory handle different languages, but will likely fail on hieroglyphic texts.

The recognition part currently uses open-source Tesseract OCR (https://code.google.com/p/tesseract-ocr/). If Tesseract OCR is not installed on your system, the corresponding part of the functionality will be unavailable.

Here are instructions on how to install Tesseract on your machine (Linux or Mac; Windows users should look for precompiled binaries or try to adopt the instructions below):

Tesseract installation instruction (Linux, Mac)

0. Linux users may try to install tesseract-3.03-rc1 (or later) and leptonica-1.70 (or later) with the corresponding development packages using their package manager. Mac users may try brew. The instructions below are for those who wants to build tesseract from source.

1. download leptonica 1.70 tarball (helper image processing library, used by tesseract. Later versions might work too): http://www.leptonica.com/download.html unpack and build it:

cd leptonica-1.70 mkdir build && cd build && ../configure && make && sudo make install

leptonica will be installed to /usr/local.

2. download tesseract-3.03-rc1 tarball from https://drive.google.com/folderview?id=0B7l10Bj_LprhQnpSRkpGMGV2eE0&usp=sharing unpack and build it:

needed only to build tesseract

export LIBLEPT_HEADERSDIR=/usr/local/include/ cd tesseract-3.03 mkdir build && cd build ../configure --with-extra-includes=/usr/local --with-extra-libraries=/usr/local make && sudo make install

Tesseract will be installed to /usr/local.

3. download the pre-trained classifier data for English language: https://code.google.com/p/tesseract-ocr/downloads/detail?name=eng.traineddata.gz

unzip it (gzip -d eng.traineddata.gz) and copy to /usr/local/share/tessdata.

Notes

1. Google announced that they close code.google.com, so at some moment in the future you may have to find Tesseract 3.03rc1 or later.

2. Tesseract configure script may fail to detect leptonica, so you may have to edit the configure script - comment off some if's around this message and retain only "then" branch.

3. You are encouraged to search the Net for some better pre-trained classifiers, as well as classifiers for other languages.

Text Detection CNN

Intro

The text module now have a text detection and recognition using deep CNN. The text detector deep CNN that takes an image which may contain multiple words. This outputs a list of Rects with bounding boxes and probability of text there. The text recognizer provides a probabillity over a given vocabulary for each of these rects.

Two backends are supported 1) caffe 2) opencv-dnn

Instalation of Caffe backend

• Please note a custom caffe based on SSD branch is required, the link of the custom caffe is provided below The caffe wrapping backend has the requirements caffe does.
• Caffe can be built against OpenCV, if the caffe backend is enabled, a circular bependency arises. The simplest solution is to build caffe without support for OpenCV.
• Only the OS supported by Caffe are supported by the backend. The scripts describing the module have been developed in ubuntu 16.04 and assume such a system. Other UNIX systems including OSX should be easy to adapt.

Sample script for building Caffe

#!/bin/bash
SRCROOT="${HOME}/caffe_inst/"
mkdir -p "$SRCROOT"
cd "$SRCROOT"
git clone https://github.com/sghoshcvc/TextBoxes.git
cd TextBoxes
cat Makefile.config.example  > Makefile.config
echo 'USE_OPENCV := 0' >> Makefile.config
echo 'INCLUDE_DIRS += /usr/include/hdf5/serial/' >> Makefile.config
echo 'LIBRARY_DIRS += /usr/lib/x86_64-linux-gnu/hdf5/serial/' >> Makefile.config


echo "--- /tmp/caffe/include/caffe/net.hpp	2017-05-28 04:55:47.929623902 +0200
+++ caffe/distribute/include/caffe/net.hpp	2017-05-28 04:51:33.437090768 +0200
@@ -234,6 +234,7 @@

     template <typename T>
     friend class Net;
+    virtual ~Callback(){}
   };
   const vector<Callback*>& before_forward() const { return before_forward_; }
   void add_before_forward(Callback* value) {
">/tmp/cleanup_caffe.diff

patch < /tmp/cleanup_caffe.diff


make -j 6

make pycaffe

make distribute

#!/bin/bash
cd $OPENCV_BUILD_DIR #You must set this
CAFFEROOT="${HOME}/caffe_inst/" #If you used the previous code to compile Caffe in ubuntu 16.04

cmake  -DCaffe_LIBS:FILEPATH="$CAFFEROOT/caffe/distribute/lib/libcaffe.so" -DBUILD_opencv_ts:BOOL="0" -DBUILD_opencv_dnn:BOOL="0" -DBUILD_opencv_dnn_modern:BOOL="0" -DCaffe_INCLUDE_DIR:PATH="$CAFFEROOT/caffe/distribute/include" -DWITH_MATLAB:BOOL="0" -DBUILD_opencv_cudabgsegm:BOOL="0"  -DWITH_QT:BOOL="1" -DBUILD_opencv_cudaoptflow:BOOL="0" -DBUILD_opencv_cudastereo:BOOL="0" -DBUILD_opencv_cudafilters:BOOL="0" -DBUILD_opencv_cudev:BOOL="1" -DOPENCV_EXTRA_MODULES_PATH:PATH="$OPENCV_CONTRIB/modules"   ./

where $OPECV_CONTRIB is the root directory containing opencv_contrib module

Instalation of Caffe backend

Use of opencv-dnn does not need any additional library.

The recent opencv-3.3.0 needs to be build with extra modules to use text module.