%global _empty_manifest_terminate_build 0 Name: python-pysseract Version: 1.3.1 Release: 1 Summary: Python binding to Tesseract API License: MIT URL: https://github.com/xiahongze/pysseract Source0: https://mirrors.nju.edu.cn/pypi/web/packages/17/5f/d94d064e54254cef5bade2ab69168e2ebff6c6edfef8998ababaca01a667/pysseract-1.3.1.tar.gz BuildArch: noarch Requires: python3-m2r %description [![Build Status](https://travis-ci.org/xiahongze/pysseract.svg?branch=master)](https://travis-ci.org/xiahongze/pysseract) [![](https://img.shields.io/badge/python-3.5+-blue.svg)](https://www.python.org/download/releases/3.5.0/) [![](https://readthedocs.org/projects/pysseract/badge/?version=latest)](https://pysseract.readthedocs.io/en/latest/?badge=latest) A Python binding to [Tesseract API](https://github.com/tesseract-ocr/tesseract). Tesseract is an open-source tool made available by Google for Optical Character Recognition (OCR) - that is, getting a computer to read the text in an image. Tesseract allows you to perform this task at a number of levels of granularity (one character at a time, one word at a time, and so on), by segmenting the page in a number of different ways (by assuming the whole page is one lump of text, or one line, or sparsely located throughout the source image), and with a number of different language models including ones you have built (pre-built models are available at https://github.com/tesseract-ocr/tessdata among other places). Pip 19.3.1 or greater is required if you're installing the wheel for this package, otherwise just install the source. On Linux, if you install the wheel Tesseract comes included. You will however need to provide the Tesseract models. An example of how you might do this with English on a linux system is as follows: ```bash curl -O https://raw.githubusercontent.com/tesseract-ocr/tessdata_fast/4.0.0/eng.traineddata mkdir -p /usr/local/share/tessdata/ && sudo mv eng.traineddata /usr/local/share/tessdata/ ``` The reason the file is being put in to `/usr/local/share/tessdata/` is because that is the default value for `TESSDATA_PREFIX`, an environment variable that Tesseract uses to locate model files. You're free to override the value of `TESSDATA_PREFIX`, of course. [Documentation](https://pysseract.readthedocs.io/en/latest/pysseract.html) is hosted on *readthedocs*. # Basic usage In order to just get all the text from an image and concatenate it into a string, run the following: ```python import pysseract t = pysseract.Pysseract() t.SetImageFromPath('tests/001-helloworld.png') print(t.utf8Text) ``` If instead you want to iterate through the text boxes found in an image at the TEXTLINE level (coarser-grained than WORD, but also lower-level than BLOCK), then you might run the following: ```python with pysseract.Pysseract() as t: boxes = [] text = [] conf = [] LEVEL = pysseract.PageIteratorLevel.TEXTLINE for box, text, confidence in t.IterAt(LEVEL): lines.append(text) boxes.append(box) confidence.append(conf) ``` A third possibility is that you may want to control how exactly the image is segmented. This is done before instantiating a `ResultIterator`, as follows: ```python with pysseract.Pysseract() as t: t.pageSegMode = pysseract.PageSegMode.SINGLE_BLOCK t.SetImageFromPath("002-quick-fox.jpg") t.SetSourceResolution(70) boxes = [] text = [] conf = [] LEVEL = pysseract.PageIteratorLevel.TEXTLINE for box, text, confidence in t.IterAt(LEVEL): lines.append(text) boxes.append(box) confidence.append(conf) ``` Finally, if you want to work with the low-level iterator built into Tesseract, the below code will work for you. This is primarily intended for people who want fine-grain control when searching through the results. For instance, if you want to look at the first paragraph, jump to the next word, then the next block after that, then the next symbol after that, you would use this approach: ```python t = pysseract.Pysseract() t.SetImageFromPath("002-quick-fox.jpg") resIter = t.GetIterator() boxes = [] lines = [] confidence = [] # First, look at the paragraph level level = pysseract.PageIteratorLevel.PARA boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) # Now the next word after the paragraph we just looked at level = pysseract.PageIteratorLevel.WORD resIter.Next(level) boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) # Now the next block level = pysseract.PageIteratorLevel.BLOCK resIter.Next(level) boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) # Lastly, look at the next symbol after the block we just looked at level = pysseract.PageIteratorLevel.SYMBOL resIter.Next(level) boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) ``` # Building the package Requirements - gcc/clang with at least c++11 support - libtesseract, libtesseract-dev (equivalent on non-Debian/Ubuntu systems) - pybind11>=2.2 ```bash python3 setup.py build install test ``` # Building the documentation ```bash pip install sphinx sphinx_rtd_theme m2r python3 setup.py build_sphinx ``` You should find the generated html in `build/sphinx`. # Contribute Look at [Tesseract BaseAPI](https://github.com/tesseract-ocr/tesseract/blob/master/src/api/baseapi.cpp) and import those functions of interest to `pymodule.cpp`. Please write a brief description in your wrapper function like those already in `pymodule.cpp`. # Reference - [basic pybind11](https://pybind11.readthedocs.io/en/master/basics.html) - [class based pybind11](https://pybind11.readthedocs.io/en/master/classes.html) - [compiling with pybind11](https://pybind11.readthedocs.io/en/master/compiling.html) # LICENSE MIT %package -n python3-pysseract Summary: Python binding to Tesseract API Provides: python-pysseract BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pysseract [![Build Status](https://travis-ci.org/xiahongze/pysseract.svg?branch=master)](https://travis-ci.org/xiahongze/pysseract) [![](https://img.shields.io/badge/python-3.5+-blue.svg)](https://www.python.org/download/releases/3.5.0/) [![](https://readthedocs.org/projects/pysseract/badge/?version=latest)](https://pysseract.readthedocs.io/en/latest/?badge=latest) A Python binding to [Tesseract API](https://github.com/tesseract-ocr/tesseract). Tesseract is an open-source tool made available by Google for Optical Character Recognition (OCR) - that is, getting a computer to read the text in an image. Tesseract allows you to perform this task at a number of levels of granularity (one character at a time, one word at a time, and so on), by segmenting the page in a number of different ways (by assuming the whole page is one lump of text, or one line, or sparsely located throughout the source image), and with a number of different language models including ones you have built (pre-built models are available at https://github.com/tesseract-ocr/tessdata among other places). Pip 19.3.1 or greater is required if you're installing the wheel for this package, otherwise just install the source. On Linux, if you install the wheel Tesseract comes included. You will however need to provide the Tesseract models. An example of how you might do this with English on a linux system is as follows: ```bash curl -O https://raw.githubusercontent.com/tesseract-ocr/tessdata_fast/4.0.0/eng.traineddata mkdir -p /usr/local/share/tessdata/ && sudo mv eng.traineddata /usr/local/share/tessdata/ ``` The reason the file is being put in to `/usr/local/share/tessdata/` is because that is the default value for `TESSDATA_PREFIX`, an environment variable that Tesseract uses to locate model files. You're free to override the value of `TESSDATA_PREFIX`, of course. [Documentation](https://pysseract.readthedocs.io/en/latest/pysseract.html) is hosted on *readthedocs*. # Basic usage In order to just get all the text from an image and concatenate it into a string, run the following: ```python import pysseract t = pysseract.Pysseract() t.SetImageFromPath('tests/001-helloworld.png') print(t.utf8Text) ``` If instead you want to iterate through the text boxes found in an image at the TEXTLINE level (coarser-grained than WORD, but also lower-level than BLOCK), then you might run the following: ```python with pysseract.Pysseract() as t: boxes = [] text = [] conf = [] LEVEL = pysseract.PageIteratorLevel.TEXTLINE for box, text, confidence in t.IterAt(LEVEL): lines.append(text) boxes.append(box) confidence.append(conf) ``` A third possibility is that you may want to control how exactly the image is segmented. This is done before instantiating a `ResultIterator`, as follows: ```python with pysseract.Pysseract() as t: t.pageSegMode = pysseract.PageSegMode.SINGLE_BLOCK t.SetImageFromPath("002-quick-fox.jpg") t.SetSourceResolution(70) boxes = [] text = [] conf = [] LEVEL = pysseract.PageIteratorLevel.TEXTLINE for box, text, confidence in t.IterAt(LEVEL): lines.append(text) boxes.append(box) confidence.append(conf) ``` Finally, if you want to work with the low-level iterator built into Tesseract, the below code will work for you. This is primarily intended for people who want fine-grain control when searching through the results. For instance, if you want to look at the first paragraph, jump to the next word, then the next block after that, then the next symbol after that, you would use this approach: ```python t = pysseract.Pysseract() t.SetImageFromPath("002-quick-fox.jpg") resIter = t.GetIterator() boxes = [] lines = [] confidence = [] # First, look at the paragraph level level = pysseract.PageIteratorLevel.PARA boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) # Now the next word after the paragraph we just looked at level = pysseract.PageIteratorLevel.WORD resIter.Next(level) boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) # Now the next block level = pysseract.PageIteratorLevel.BLOCK resIter.Next(level) boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) # Lastly, look at the next symbol after the block we just looked at level = pysseract.PageIteratorLevel.SYMBOL resIter.Next(level) boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) ``` # Building the package Requirements - gcc/clang with at least c++11 support - libtesseract, libtesseract-dev (equivalent on non-Debian/Ubuntu systems) - pybind11>=2.2 ```bash python3 setup.py build install test ``` # Building the documentation ```bash pip install sphinx sphinx_rtd_theme m2r python3 setup.py build_sphinx ``` You should find the generated html in `build/sphinx`. # Contribute Look at [Tesseract BaseAPI](https://github.com/tesseract-ocr/tesseract/blob/master/src/api/baseapi.cpp) and import those functions of interest to `pymodule.cpp`. Please write a brief description in your wrapper function like those already in `pymodule.cpp`. # Reference - [basic pybind11](https://pybind11.readthedocs.io/en/master/basics.html) - [class based pybind11](https://pybind11.readthedocs.io/en/master/classes.html) - [compiling with pybind11](https://pybind11.readthedocs.io/en/master/compiling.html) # LICENSE MIT %package help Summary: Development documents and examples for pysseract Provides: python3-pysseract-doc %description help [![Build Status](https://travis-ci.org/xiahongze/pysseract.svg?branch=master)](https://travis-ci.org/xiahongze/pysseract) [![](https://img.shields.io/badge/python-3.5+-blue.svg)](https://www.python.org/download/releases/3.5.0/) [![](https://readthedocs.org/projects/pysseract/badge/?version=latest)](https://pysseract.readthedocs.io/en/latest/?badge=latest) A Python binding to [Tesseract API](https://github.com/tesseract-ocr/tesseract). Tesseract is an open-source tool made available by Google for Optical Character Recognition (OCR) - that is, getting a computer to read the text in an image. Tesseract allows you to perform this task at a number of levels of granularity (one character at a time, one word at a time, and so on), by segmenting the page in a number of different ways (by assuming the whole page is one lump of text, or one line, or sparsely located throughout the source image), and with a number of different language models including ones you have built (pre-built models are available at https://github.com/tesseract-ocr/tessdata among other places). Pip 19.3.1 or greater is required if you're installing the wheel for this package, otherwise just install the source. On Linux, if you install the wheel Tesseract comes included. You will however need to provide the Tesseract models. An example of how you might do this with English on a linux system is as follows: ```bash curl -O https://raw.githubusercontent.com/tesseract-ocr/tessdata_fast/4.0.0/eng.traineddata mkdir -p /usr/local/share/tessdata/ && sudo mv eng.traineddata /usr/local/share/tessdata/ ``` The reason the file is being put in to `/usr/local/share/tessdata/` is because that is the default value for `TESSDATA_PREFIX`, an environment variable that Tesseract uses to locate model files. You're free to override the value of `TESSDATA_PREFIX`, of course. [Documentation](https://pysseract.readthedocs.io/en/latest/pysseract.html) is hosted on *readthedocs*. # Basic usage In order to just get all the text from an image and concatenate it into a string, run the following: ```python import pysseract t = pysseract.Pysseract() t.SetImageFromPath('tests/001-helloworld.png') print(t.utf8Text) ``` If instead you want to iterate through the text boxes found in an image at the TEXTLINE level (coarser-grained than WORD, but also lower-level than BLOCK), then you might run the following: ```python with pysseract.Pysseract() as t: boxes = [] text = [] conf = [] LEVEL = pysseract.PageIteratorLevel.TEXTLINE for box, text, confidence in t.IterAt(LEVEL): lines.append(text) boxes.append(box) confidence.append(conf) ``` A third possibility is that you may want to control how exactly the image is segmented. This is done before instantiating a `ResultIterator`, as follows: ```python with pysseract.Pysseract() as t: t.pageSegMode = pysseract.PageSegMode.SINGLE_BLOCK t.SetImageFromPath("002-quick-fox.jpg") t.SetSourceResolution(70) boxes = [] text = [] conf = [] LEVEL = pysseract.PageIteratorLevel.TEXTLINE for box, text, confidence in t.IterAt(LEVEL): lines.append(text) boxes.append(box) confidence.append(conf) ``` Finally, if you want to work with the low-level iterator built into Tesseract, the below code will work for you. This is primarily intended for people who want fine-grain control when searching through the results. For instance, if you want to look at the first paragraph, jump to the next word, then the next block after that, then the next symbol after that, you would use this approach: ```python t = pysseract.Pysseract() t.SetImageFromPath("002-quick-fox.jpg") resIter = t.GetIterator() boxes = [] lines = [] confidence = [] # First, look at the paragraph level level = pysseract.PageIteratorLevel.PARA boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) # Now the next word after the paragraph we just looked at level = pysseract.PageIteratorLevel.WORD resIter.Next(level) boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) # Now the next block level = pysseract.PageIteratorLevel.BLOCK resIter.Next(level) boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) # Lastly, look at the next symbol after the block we just looked at level = pysseract.PageIteratorLevel.SYMBOL resIter.Next(level) boxes.append(resIter.BoundingBox(level)) lines.append(resIter.GetUTF8Text(level)) confidence.append(resIter.Confidence(level)) ``` # Building the package Requirements - gcc/clang with at least c++11 support - libtesseract, libtesseract-dev (equivalent on non-Debian/Ubuntu systems) - pybind11>=2.2 ```bash python3 setup.py build install test ``` # Building the documentation ```bash pip install sphinx sphinx_rtd_theme m2r python3 setup.py build_sphinx ``` You should find the generated html in `build/sphinx`. # Contribute Look at [Tesseract BaseAPI](https://github.com/tesseract-ocr/tesseract/blob/master/src/api/baseapi.cpp) and import those functions of interest to `pymodule.cpp`. Please write a brief description in your wrapper function like those already in `pymodule.cpp`. # Reference - [basic pybind11](https://pybind11.readthedocs.io/en/master/basics.html) - [class based pybind11](https://pybind11.readthedocs.io/en/master/classes.html) - [compiling with pybind11](https://pybind11.readthedocs.io/en/master/compiling.html) # LICENSE MIT %prep %autosetup -n pysseract-1.3.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pysseract -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 15 2023 Python_Bot - 1.3.1-1 - Package Spec generated