【Python】Mac安装Tesseract详解

于 2025-04-13 19:20:49 发布
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分类专栏： 机器视觉 Python 文章标签： ocr opencv 人工智能 计算机视觉 python 开发语言 java
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本文链接：https://blog.csdn.net/zhyooo123/article/details/147190210
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安装OpenCV 4.0和其他依赖库

pip install opencv-python
pip install numpy
pip install matplotlib

一、OCR概述

OCR（Optical Character Recognition，光学字符识别）是一种将图像中的文字转换为机器可读文本的技术。它广泛应用于扫描文档、车牌识别、手写数字识别等领域。

OCR的核心任务是从图像中识别出字符并转化为文本。在这个过程中，图像中的文本部分需要被提取、清晰化并转换为二值图像，再通过OCR模型识别出文字。

OCR的基本流程

OCR的基本流程通常包括以下几个步骤：

  • 图像预处理：去噪、增强对比度、调整光照等。
  • 区域检测：检测文本区域或单个字符。
  • 字符分割：将文本区域分割为单个字符或单词。 字符识别：通过OCR模型识别字符。

二、Tesseract

OpenCV本身不具备OCR识别功能，但可以通过与Tesseract结合使用，Tesseract是一个开源的OCR（Optical Character Recognition）引擎，OCR是一种技术，它可以识别和解析图像中的文本内容，使计算机能够理解并处理这些文本。Tesseract提供了丰富的配置选项和接口，使得开发者可以根据自己的需求和场景进行定制化和集成。通过使用Tesseract，你可以将一张包含文字的图像（如扫描文档、照片或截屏）输入到引擎中，然后Tesseract会通过一系列的图像处理和模式识别技术来提取出图像中的文本信息。它将识别出的文本转换为可以被计算机编辑和搜索的文本内容。

简单来说，Tesseract是一个强大的OCR引擎，适用于将图像中的文字提取出来，并将其转换为计算机可处理的文本形式。它在许多领域和应用中被广泛使用，如扫描和数字化文档、自动化数据输入、图书馆和档案管理等。

在Python中安装Tesseract的方法包括：安装Tesseract OCR软件、使用Python库pytesseract、设置环境变量。 

2.1 下载并安装Tesseract

根据你的操作系统，访问Tesseract OCR的GitHub页面或其官方网站，下载适合你的系统的安装包。对于Windows用户，通常提供一个可执行的安装文件，而Linux和macOS用户可以通过包管理器安装。

Windows：下载并运行Windows安装程序，按照提示完成安装。
macOS：使用Homebrew可以轻松安装，运行命令 brew install tesseract。
Linux：根据你的Linux发行版，使用包管理器（例如apt、yum）安装，例如 sudo apt-get install tesseract-ocr。

brew install tesseract执行结果： 

((myenv) ) 192:test csguo007$ brew install tesseract
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Warning: You are using macOS 12.
We (and Apple) do not provide support for this old version.

This is a Tier 3 configuration:
  https://docs.brew.sh/Support-Tiers#tier-3
Do not report any issues to Homebrew/* repositories!
Read the above document instead before opening any issues or PRs.

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==> Downloading https://github.com/fribidi/fribidi/releases/download/v1.0.16/fri
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/5c6a1acb824bb8ade4e4b6a865bbcb0e7487f290bee60c65febcfc4a292415aa--fribidi-1.0.16.tar.xz
==> Fetching pango
==> Downloading https://raw.githubusercontent.com/Homebrew/homebrew-core/a0ecebd
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==> Fetching tesseract
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==> Downloading https://github.com/tesseract-ocr/tessdata_fast/raw/4.1.0/eng.tra
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==> Downloading https://github.com/USCDataScience/counterfeit-electronics-tesser
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==> Downloading https://github.com/tesseract-ocr/tesseract/archive/refs/tags/5.5
==> Downloading from https://codeload.github.com/tesseract-ocr/tesseract/tar.gz/
                    -=O=-            #     #     #     #                        
==> Installing dependencies for tesseract: libpng, fontconfig, pcre2, libunistring, gettext, bison, expat, python@3.13, meson, python-setuptools, glib, xorgproto, util-macros, libxau, libxdmcp, xcb-proto, libxcb, xtrans, libx11, libxext, libxrender, lzo, pixman, cairo, graphite2, icu4c@77, gobject-introspection, harfbuzz, giflib, cmake, nasm, jpeg-turbo, zstd, libtiff, little-cms2, doxygen, openjpeg, webp, leptonica, libb2, libarchive, fribidi and pango
==> Installing tesseract dependency: libpng
==> ./configure --disable-silent-rules
==> make
==> make test
==> make install
==> Downloading https://formulae.brew.sh/api/formula.jws.json
🍺  /usr/local/Cellar/libpng/1.6.47: 28 files, 1.3MB, built in 1 minute 34 seconds
==> Installing tesseract dependency: fontconfig
==> ./configure --disable-silent-rules --disable-docs --enable-static --with-add
==> make install RUN_FC_CACHE_TEST=false
==> Regenerating font cache, this may take a while
==> /usr/local/Cellar/fontconfig/2.16.0/bin/fc-cache -frv
🍺  /usr/local/Cellar/fontconfig/2.16.0: 69 files, 1.4MB, built in 58 seconds
==> Installing tesseract dependency: pcre2
==> ./configure --enable-pcre2-16 --enable-pcre2-32 --enable-pcre2grep-libz --en
==> make
==> make install
🍺  /usr/local/Cellar/pcre2/10.45: 242 files, 7.0MB, built in 1 minute
==> Installing tesseract dependency: libunistring
==> ./configure --disable-silent-rules
==> make
==> make check
==> make install
==> Downloading https://formulae.brew.sh/api/formula.jws.json
🍺  /usr/local/Cellar/libunistring/1.3: 59 files, 5.5MB, built in 18 minutes 52 seconds
==> Installing tesseract dependency: gettext
==> ./configure --with-libunistring-prefix=/usr/local/opt/libunistring --disable
==> make
==> make install
🍺  /usr/local/Cellar/gettext/0.24: 2,189 files, 19.7MB, built in 12 minutes 24 seconds
==> Installing tesseract dependency: bison
==> Downloading https://ghcr.io/v2/homebrew/core/bison/manifests/3.8.2
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/0a84b14c20dfba4609542ea4b14a4eb93d369f7f83f373b568017fc7d76b6505--bison-3.8.2.bottle_manifest.json
==> Pouring bison--3.8.2.monterey.bottle.tar.gz
🍺  /usr/local/Cellar/bison/3.8.2: 100 files, 3.7MB
==> Installing tesseract dependency: expat
==> Downloading https://formulae.brew.sh/api/formula.jws.json
==> ./configure --mandir=/usr/local/Cellar/expat/2.7.1/share/man
==> make install
🍺  /usr/local/Cellar/expat/2.7.1: 23 files, 644.5KB, built in 27 seconds
==> Installing tesseract dependency: python@3.13
==> Patching
==> Applying 3.13-sysconfig.diff
==> ./configure --enable-ipv6 --datarootdir=/usr/local/Cellar/python@3.13/3.13.3
==> make
==> make install PYTHONAPPSDIR=/usr/local/Cellar/python@3.13/3.13.3
==> make frameworkinstallextras PYTHONAPPSDIR=/usr/local/Cellar/python@3.13/3.13
==> /usr/local/Cellar/python@3.13/3.13.3/bin/python3.13 -m venv /private/tmp/pyt
==> /private/tmp/pythonA3.13-20250413-9121-vsl8vb/Python-3.13.3/whl_build/bin/pi
==> /private/tmp/pythonA3.13-20250413-9121-vsl8vb/Python-3.13.3/whl_build/bin/pi
==> /private/tmp/pythonA3.13-20250413-9121-vsl8vb/Python-3.13.3/whl_build/bin/pi
==> /private/tmp/pythonA3.13-20250413-9121-vsl8vb/Python-3.13.3/whl_build/bin/pi
==> /private/tmp/pythonA3.13-20250413-9121-vsl8vb/Python-3.13.3/whl_build/bin/pi
==> /usr/local/Cellar/python@3.13/3.13.3/bin/python3.13 -Im ensurepip
==> /usr/local/Cellar/python@3.13/3.13.3/bin/python3.13 -Im pip install -v --no-
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They have been backed up to: /Users/csguo007/Library/Caches/Homebrew/Backup
==> Summary
🍺  /usr/local/Cellar/python@3.13/3.13.3: 9,261 files, 205.5MB, built in 24 minutes 32 seconds
==> Installing tesseract dependency: meson
==> Downloading https://ghcr.io/v2/homebrew/core/meson/manifests/1.7.2
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/c3f22f8dd50f276d5582ead8b3037c14e866e7a379c4b657e5053da00bf63249--meson-1.7.2.bottle_manifest.json
==> Pouring meson--1.7.2.all.bottle.tar.gz
🍺  /usr/local/Cellar/meson/1.7.2: 259 files, 3.7MB
==> Installing tesseract dependency: python-setuptools
==> Downloading https://ghcr.io/v2/homebrew/core/python-setuptools/manifests/78.
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/173395a4481e0d9f87ef5794b350aed2cb72c56c9f11ed174abf2e7466a4d0b7--python-setuptools-78.1.0.bottle_manifest.json
==> Pouring python-setuptools--78.1.0.all.bottle.tar.gz
🍺  /usr/local/Cellar/python-setuptools/78.1.0: 1,004 files, 8.4MB
==> Installing tesseract dependency: glib
==> Downloading https://formulae.brew.sh/api/formula.jws.json
==> Patching
==> Applying hardcoded-paths.diff
==> meson setup build_staging -Dintrospection=disabled --localstatedir=/usr/loca
==> meson compile -C build_staging --verbose
==> meson install -C build_staging
==> meson setup build -Dcairo=disabled -Ddoctool=disabled --prefix=/private/tmp/
==> meson compile -C build --verbose
==> meson install -C build
==> meson setup build --default-library=both -Dintrospection=enabled --localstat
==> meson compile -C build --verbose
==> meson install -C build
🍺  /usr/local/Cellar/glib/2.84.0_1: 504 files, 35.5MB, built in 5 minutes 28 seconds
==> Installing tesseract dependency: xorgproto
==> Downloading https://ghcr.io/v2/homebrew/core/xorgproto/manifests/2024.1
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/d89d69340366d1d015e7214520f64081e2d82d7955fb28c6ea5f6539dcb610b2--xorgproto-2024.1.bottle_manifest.json
==> Pouring xorgproto--2024.1.monterey.bottle.tar.gz
🍺  /usr/local/Cellar/xorgproto/2024.1: 268 files, 3.9MB
==> Installing tesseract dependency: util-macros
==> ./configure --disable-silent-rules --sysconfdir=/usr/local/etc --localstated
==> make install
🍺  /usr/local/Cellar/util-macros/1.20.2: 9 files, 177.7KB, built in 5 seconds
==> Installing tesseract dependency: libxau
==> ./configure --sysconfdir=/usr/local/etc --localstatedir=/usr/local/var --dis
==> make
==> make install
🍺  /usr/local/Cellar/libxau/1.0.12: 21 files, 115.5KB, built in 19 seconds
==> Installing tesseract dependency: libxdmcp
==> Downloading https://ghcr.io/v2/homebrew/core/libxdmcp/manifests/1.1.5
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/8713e53f73d0be64780dbea522b0ee07027e6cc7828ff1a4fa0a6596f14cfe25--libxdmcp-1.1.5.bottle_manifest.json
==> Pouring libxdmcp--1.1.5.monterey.bottle.tar.gz
🍺  /usr/local/Cellar/libxdmcp/1.1.5: 12 files, 123.5KB
==> Installing tesseract dependency: xcb-proto
==> Downloading https://ghcr.io/v2/homebrew/core/xcb-proto/manifests/1.17.0-1
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/ebd0685a03738108ecd5bbfd723d384ec6be2e0e592eb3efa48bd482f8d08f79--xcb-proto-1.17.0-1.bottle_manifest.json
==> Pouring xcb-proto--1.17.0.all.bottle.1.tar.gz
🍺  /usr/local/Cellar/xcb-proto/1.17.0: 48 files, 793.4KB
==> Installing tesseract dependency: libxcb
==> ./configure --sysconfdir=/usr/local/etc --localstatedir=/usr/local/var --ena
==> make
==> make install
🍺  /usr/local/Cellar/libxcb/1.17.0: 2,498 files, 7MB, built in 50 seconds
==> Installing tesseract dependency: xtrans
==> Downloading https://ghcr.io/v2/homebrew/core/xtrans/manifests/1.6.0
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/1586b850573c3a0929e403c05076ce91ad2909c4673e9b1ffdacb264334196c0--xtrans-1.6.0.bottle_manifest.json
==> Pouring xtrans--1.6.0.all.bottle.tar.gz
🍺  /usr/local/Cellar/xtrans/1.6.0: 16 files, 295KB
==> Installing tesseract dependency: libx11
==> ./configure --sysconfdir=/usr/local/etc --localstatedir=/usr/local/var --dis
==> make
==> make install
🍺  /usr/local/Cellar/libx11/1.8.12: 1,043 files, 7.0MB, built in 1 minute 49 seconds
==> Installing tesseract dependency: libxext
==> Downloading https://ghcr.io/v2/homebrew/core/libxext/manifests/1.3.6
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/fedb843acbfcf682fb451396914bfc096f9ea2748376d4c43ce2af7ce1b766b7--libxext-1.3.6.bottle_manifest.json
==> Pouring libxext--1.3.6.monterey.bottle.tar.gz
🍺  /usr/local/Cellar/libxext/1.3.6: 88 files, 436.2KB
==> Installing tesseract dependency: libxrender
==> Downloading https://formulae.brew.sh/api/formula.jws.json
==> ./configure --sysconfdir=/usr/local/etc --localstatedir=/usr/local/var --dis
==> make
==> make install
🍺  /usr/local/Cellar/libxrender/0.9.12: 13 files, 205.8KB, built in 19 seconds
==> Installing tesseract dependency: lzo
==> Downloading https://ghcr.io/v2/homebrew/core/lzo/manifests/2.10
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/d4aa5b0c239912c53bc857d1012c6b7feb4acb509618f5e100f95bf8521f08e7--lzo-2.10.bottle_manifest.json
==> Pouring lzo--2.10.monterey.bottle.tar.gz
🍺  /usr/local/Cellar/lzo/2.10: 32 files, 575.3KB
==> Installing tesseract dependency: pixman
==> meson setup build
==> meson compile -C build --verbose
==> meson install -C build
🍺  /usr/local/Cellar/pixman/0.44.2: 10 files, 640.4KB, built in 23 seconds
==> Installing tesseract dependency: cairo
==> meson setup build -Dfontconfig=enabled -Dfreetype=enabled -Dpng=enabled -Dgl
==> meson compile -C build --verbose
==> meson install -C build
🍺  /usr/local/Cellar/cairo/1.18.4: 53 files, 2MB, built in 1 minute 20 seconds
==> Installing tesseract dependency: graphite2
==> Downloading https://ghcr.io/v2/homebrew/core/graphite2/manifests/1.3.14
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/bbb4dd2ef1846301d1eb08053e19e11ca9c780f93f4d3b2d638fd94a9bf54a0c--graphite2-1.3.14.bottle_manifest.json
==> Pouring graphite2--1.3.14.monterey.bottle.tar.gz
🍺  /usr/local/Cellar/graphite2/1.3.14: 19 files, 278.7KB
==> Installing tesseract dependency: icu4c@77
==> ./configure --disable-samples --disable-tests --enable-static --with-library
==> make
==> make install
🍺  /usr/local/Cellar/icu4c@77/77.1: 277 files, 80.6MB, built in 5 minutes 28 seconds
==> Installing tesseract dependency: gobject-introspection
==> Downloading https://formulae.brew.sh/api/formula.jws.json
==> Patching
==> python3.13 -m venv --system-site-packages --without-pip /usr/local/Cellar/go
==> python3.13 -m pip --python=/usr/local/Cellar/gobject-introspection/1.84.0/li
==> python3.13 -m pip --python=/usr/local/Cellar/gobject-introspection/1.84.0/li
==> python3.13 -m pip --python=/usr/local/Cellar/gobject-introspection/1.84.0/li
==> python3.13 -m pip --python=/usr/local/Cellar/gobject-introspection/1.84.0/li
==> meson setup build -Dpython=/usr/local/Cellar/gobject-introspection/1.84.0/li
==> meson compile -C build --verbose
==> meson install -C build
🍺  /usr/local/Cellar/gobject-introspection/1.84.0: 781 files, 7.4MB, built in 1 minute 21 seconds
==> Installing tesseract dependency: harfbuzz
==> meson setup build --default-library=both -Dcairo=enabled -Dcoretext=enabled 
==> meson compile -C build --verbose
==> meson install -C build
🍺  /usr/local/Cellar/harfbuzz/11.0.1: 76 files, 10.6MB, built in 1 minute 49 seconds
==> Installing tesseract dependency: giflib
==> Downloading https://ghcr.io/v2/homebrew/core/giflib/manifests/5.2.2
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/283773c4d2db4fe867419d7eea6811a6417889d78fad8871041c07f49b22d2a1--giflib-5.2.2.bottle_manifest.json
==> Pouring giflib--5.2.2.monterey.bottle.tar.gz
🍺  /usr/local/Cellar/giflib/5.2.2: 20 files, 590.5KB
==> Installing tesseract dependency: cmake
==> ./bootstrap --prefix=/usr/local/Cellar/cmake/4.0.1 --no-system-libs --parall
==> make
==> make install
==> Downloading https://formulae.brew.sh/api/formula.jws.json
🍺  /usr/local/Cellar/cmake/4.0.1: 3,870 files, 64.3MB, built in 18 minutes 50 seconds
==> Installing tesseract dependency: nasm
==> Downloading https://ghcr.io/v2/homebrew/core/nasm/manifests/2.16.03
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/c91540cc3a88754e02708bdc11174838450e2f956ef63d960f3a4cae089ef655--nasm-2.16.03.bottle_manifest.json
==> Pouring nasm--2.16.03.monterey.bottle.tar.gz
🍺  /usr/local/Cellar/nasm/2.16.03: 12 files, 2.8MB
==> Installing tesseract dependency: jpeg-turbo
==> cmake -S . -B build -DWITH_JPEG8=1 -DCMAKE_EXE_LINKER_FLAGS=-Wl,-rpath,@load
==> cmake --build build
==> ctest --test-dir build --rerun-failed --output-on-failure --parallel 4
==> cmake --install build
🍺  /usr/local/Cellar/jpeg-turbo/3.1.0: 47 files, 5.6MB, built in 2 minutes 39 seconds
==> Installing tesseract dependency: zstd
==> cmake -S build/cmake -B builddir -DZSTD_PROGRAMS_LINK_SHARED=ON -DZSTD_BUILD
==> cmake --build builddir
==> cmake --install builddir
🍺  /usr/local/Cellar/zstd/1.5.7: 32 files, 3.3MB, built in 46 seconds
==> Installing tesseract dependency: libtiff
==> ./configure --disable-webp --enable-zstd --enable-lzma --with-jpeg-include-d
==> make install
🍺  /usr/local/Cellar/libtiff/4.7.0: 486 files, 8.1MB, built in 48 seconds
==> Installing tesseract dependency: little-cms2
==> ./configure
==> make install
🍺  /usr/local/Cellar/little-cms2/2.17: 23 files, 1.3MB, built in 28 seconds
==> Installing tesseract dependency: doxygen
==> cmake -S . -B build -DPYTHON_EXECUTABLE=/usr/bin/python3
==> cmake --build build


==> cmake --install build
==> cmake -S . -B build -Dbuild_doc=1
🍺  /usr/local/Cellar/doxygen/1.13.2: 10 files, 25.5MB, built in 5 minutes 42 seconds
==> Installing tesseract dependency: openjpeg
==> Downloading https://formulae.brew.sh/api/formula.jws.json
==> cmake -S . -B build -DCMAKE_INSTALL_RPATH=@loader_path/../lib -DBUILD_DOC=ON
==> cmake --build build
==> cmake --install build
🍺  /usr/local/Cellar/openjpeg/2.5.3: 544 files, 14.9MB, built in 32 seconds
==> Installing tesseract dependency: webp
==> cmake -S . -B build -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_RPATH=@loader_pat
==> cmake --build build
==> cmake --install build
==> cmake -S . -B static -DBUILD_SHARED_LIBS=OFF -DCMAKE_INSTALL_RPATH=@loader_p
==> cmake --build static
🍺  /usr/local/Cellar/webp/1.5.0: 64 files, 3.6MB, built in 1 minute 8 seconds
==> Installing tesseract dependency: leptonica
==> ./configure --with-libwebp --with-libopenjpeg
==> make install
🍺  /usr/local/Cellar/leptonica/1.85.0: 57 files, 6.2MB, built in 1 minute 56 seconds
==> Installing tesseract dependency: libb2
==> Downloading https://ghcr.io/v2/homebrew/core/libb2/manifests/0.98.1
Already downloaded: /Users/csguo007/Library/Caches/Homebrew/downloads/9bb23dca331f0d74876fa5c769113885b92af8a741a851cdfe99f6dfd420b12d--libb2-0.98.1.bottle_manifest.json
==> Pouring libb2--0.98.1.monterey.bottle.tar.gz
🍺  /usr/local/Cellar/libb2/0.98.1: 9 files, 274.2KB
==> Installing tesseract dependency: libarchive
==> ./configure --without-lzo2 --without-nettle --without-xml2 --without-openssl
==> make install
🍺  /usr/local/Cellar/libarchive/3.7.9: 65 files, 3.8MB, built in 1 minute 38 seconds
==> Installing tesseract dependency: fribidi
==> ./configure --disable-silent-rules --enable-static
==> make install
🍺  /usr/local/Cellar/fribidi/1.0.16: 68 files, 530.9KB, built in 20 seconds
==> Installing tesseract dependency: pango
==> meson setup build -Ddefault_library=both -Dintrospection=enabled -Dfontconfi
==> meson compile -C build --verbose
==> meson install -C build
🍺  /usr/local/Cellar/pango/1.56.3: 69 files, 3.3MB, built in 48 seconds
==> Installing tesseract
==> ./autogen.sh
==> ./configure --datarootdir=/usr/local/share --disable-silent-rules
==> make training
==> make install training-install datarootdir=/usr/local/Cellar/tesseract/5.5.0_
==> Caveats
This formula contains only the "eng", "osd", and "snum" language data files.
If you need any other supported languages, run `brew install tesseract-lang`.
==> Summary
🍺  /usr/local/Cellar/tesseract/5.5.0_1: 75 files, 32.5MB, built in 5 minutes 29 seconds
==> Running `brew cleanup tesseract`...
Disable this behaviour by setting HOMEBREW_NO_INSTALL_CLEANUP.
Hide these hints with HOMEBREW_NO_ENV_HINTS (see `man brew`).
Removing: /Users/csguo007/Library/Caches/Homebrew/tesseract--5.5.0.tar.gz... (1.8MB)
Removing: /Users/csguo007/Library/Caches/Homebrew/tesseract--eng--4.1.0.traineddata... (3.9MB)
Removing: /Users/csguo007/Library/Caches/Homebrew/tesseract--osd--4.1.0.traineddata... (10MB)
Removing: /Users/csguo007/Library/Caches/Homebrew/tesseract--snum--5.5.0.traineddata... (8MB)
==> Caveats
==> tesseract
This formula contains only the "eng", "osd", and "snum" language data files.
If you need any other supported languages, run `brew install tesseract-lang`

2.2 验证安装结果

 成功安装后查看tesseract版本

tesseract --version
或
tesseract -v

结果如下： 

tesseract --version
tesseract 5.5.0
 leptonica-1.85.0
  libgif 5.2.2 : libjpeg 8d (libjpeg-turbo 3.1.0) : libpng 1.6.47 : libtiff 4.7.0 : zlib 1.2.11 : libwebp 1.5.0 : libopenjp2 2.5.3
 Found AVX2
 Found AVX
 Found FMA
 Found SSE4.1
 Found libarchive 3.7.9 zlib/1.2.11 liblzma/5.8.1 bz2lib/1.0.8 liblz4/1.10.0 libzstd/1.5.7
 Found libcurl/8.7.1 SecureTransport (LibreSSL/3.3.6) zlib/1.2.11 nghttp2/1.45.1

2.3 安装 Tesseract训练工具

使用 homebrew 安装 Tesseract 同时安装训练工具

brew install –with-training-tools tesseract

虽然官方提供了多种语言的识别数据包，但是在很多情况下都需要在其基础上自己进行训练，所以记得安装训练工具

 2.4 安装过程中遇到的问题

2.4.1 错误一
安装tesseract的过程中报缺少依赖的错误
Error: No such file or directory @ rb_sysopen - /Users/f/Library/Caches/Homebrew/downloads/266702d9bc59c9dfde27ce555b4a3f9ed9d0de770ba697e62a111d74ee0a4231–openjpeg-2.4.0.catalina.bottle.tar.gz
针对这类错误单独安装缺少的包即可

brew install openjpeg
2.4.2 错误二
单独安装依赖出现如下提示：
Disable this behaviour by setting HOMEBREW_NO_INSTALL_CLEANUP. Hide these hints with HOMEBREW_NO_ENV_HINTS (see man brew).
执行如下命令即可：
export HOMEBREW_NO_INSTALL_CLEANUP=TRUE

2.5 中文支持

Tesseract OCR 是一个开源的OCR（光学字符识别）引擎，它主要支持多种语言的文本识别，包括英语、西班牙语、法语等。但是，对于中文的支持，Tesseract 默认并不包含中文语言包。要使用Tesseract来识别中文文本，你需要进行一些额外的配置和安装。

2.5.1 下载中文语言包

Tesseract默认不支持中文，需要单独下载中文包。

Tesseract 的语言包需要单独下载和安装。对于中文，你可以从 Tesseract 的 GitHub 页面或者其他资源站点下载中文语言包（通常是 chi_sim 或 chi_tra，分别对应简体中文和繁体中文）。

下载地址：GitCode - 全球开发者的开源社区,开源代码托管平台

2.5.2 安装中文语言包

中文包存放目录：/usr/local/Cellar/tesseract/{tesseract版本}/share/tessdata

cd /usr/local/Cellar/tesseract/5.5.0_1/share/tessdata

 

2.5.3 查看全部语言库
tesseract --list-langs

显示结果 

tesseract --list-langs
List of available languages in "/usr/local/share/tessdata/" (8):
chi_sim
chi_tra
chr
eng
osd
snum

三、安装pytesseract和pillow

pytesseract是一个Python库，用于与Tesseract OCR引擎进行交互，它是Python的一个封装接口，使得在Python中调用Tesseract的功能更加方便。

3.1 安装pytesseract

使用pip命令来安装pytesseract库：

pip3 install pytesseract

验证安装

在Python环境中，尝试导入pytesseract以验证安装是否成功：

import pytesseract

如果没有报错，则说明安装成功。

3.2 安装 pillow

pillow主要用于处理图像，安装命令

pip3 install pillow

四、使用案例

 下面使用Tesseract进行OCR识别的简单例子

4.1 openCV读取照片识别

import cv2
import pytesseract
# 读取图片
img = cv2.imread('3.png') 
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#识别文字
text = pytesseract.image_to_string(img)
#打印文字
print(text)

4.2  pillow读取图片进行识别

import pytesseract
from PIL import Image
# 读取图片
im = Image.open('3.png')
# 识别文字，并指定语言
string = pytesseract.image_to_string(im, lang='chi_sim')
print(string)

4.3 处理不同语言

Tesseract支持多种语言，但需要安装对应的语言包。在识别文本时，可以通过参数指定语言：

text = pytesseract.image_to_string(image, lang='eng')

其中 'eng' 是语言代码，表示英语，其他语言代码可以在Tesseract文档中找到。 

eng：英语
chi_sim：简体中文
chi_tra：中文繁体

4.4 识别特定区域

如果只想识别图像中的特定区域，可以通过传递一个box参数来指定：

text = pytesseract.image_to_string(image, lang='eng', config='--psm 6', boxes='x1 y1 x2 y2')

其中 x1, y1, x2, y2 定义了识别区域的坐标。

4.5 处理PDF文件

 Tesseract也支持PDF文件的处理，可以使用 tesseract 命令行工具将PDF转换为文本。

tesseract example.pdf output.txt

4.6 调整识别参数

Tesseract提供了许多可调节的参数，可以通过pytesseract传递 config 参数来进行调整。例如，调整页面解析模式（PSM）：

text = pytesseract.image_to_string(image, config='--psm 6')

五、基本命令 

tesseract -v：查看版本
tesseract –help：查看帮助

进行识别的命令
-------------
tesseract in.jpg out.txt -l chi_sim -psm 6

   in.jpg:需要识别的图片
  out.txt:图片的结果输出到的位置
       -l:后跟用于进行识别的数据包，这里是中文
     -psm:识别方式

----------------
tesseract --list-langs：查看已有的语言识别包

psm的参数

psm 的参数很重要，表示 tesseract 识别图像的方式，比如说是一行一行识别还是逐字识别。希望逐字识别可以使用 -psm 10，希望逐行识别可以使用 -psm 6,其他没怎么用以后有机会补充。

tesseract --help-psm
  0    Orientation and script detection (OSD) only.
  1    Automatic page segmentation with OSD.
  2    Automatic page segmentation, but no OSD, or OCR.
  3    Fully automatic page segmentation, but no OSD. (Default)
  4    Assume a single column of text of variable sizes.
  5    Assume a single uniform block of vertically aligned text.
  6    Assume a single uniform block of text.
  7    Treat the image as a single text line.
  8    Treat the image as a single word.
  9    Treat the image as a single word in a circle.
 10    Treat the image as a single character.

解释

方向和脚本检测（OSD）。
  1自动页面分割与OSD。
  2自动页面分割，但没有OSD或OCR。
  3全自动页面分割，但没有OSD。 （默认）
  4假设单列可变大小的文本。
  5假设一个垂直对齐的文本的统一块。
  6假设单个统一的文本块。
  7将图像视为单个文本行。
  8将图像视为单个字。
  9将图像视为一个单个的单词。
 10将图像视为单个字符。

六、训练与识别

要使用Tesseract-OCR识别中文，首先需要准备中文字库。常用的中文字库有chi_sim.traindata和chi_tra.traindata等。可以从Tesseract-OCR的官方网站或者其他可靠的资源下载这些字库文件。

下载地址：GitCode - 全球开发者的开源社区,开源代码托管平台

下载好中文字库后，需要将其放置在Tesseract-OCR项目的tessdata文件夹中。确保Tesseract-OCR的路径已经添加到系统的环境变量中，这样在命令行中可以直接调用Tesseract-OCR的命令。

6.1 识别

tesseract libai.jpg libai -l chi_sim -psm 6
抹甫曰月闫充，
屁是讹上霜;
亭孰塑明汛
佃爽崽故歹o

显然准确率不高，进行训练。

6.2 训练自己的字库

如果现有的中文字库无法满足需求，可以训练自己的字库。Tesseract-OCR提供了训练字库的工具，可以使用该工具来训练自己的中文字库。训练过程需要一些机器学习的知识，具体可以参考Tesseract-OCR的官方文档。
在训练自己的字库时，需要准备大量的中文文本数据。使用这些数据训练字库后，Tesseract-OCR将能够更好地识别中文文本。

1.生成字体文件

打开jTessBoxEditor工具，菜单栏：tools->Merge TIFF…，选中要合成的图片并保存为为：libai.tif

2.生成 box 文件

tesseract libai.tif libai -l chi_sim -psm 6 batch.nochop makebox

3.利用 jTessBoxEditor 校正

校正之后记得保存。

4.生成.tr文件

tesseract libai.tif libai -psm 6 nobatch box.train

5.生成unicharset文件

unicharset_extractor libai.box

6.创建font_properties文件

echo ‘font 0 0 0 0 0’ > font_properties

7.training

shapeclustering -F font_properties -U unicharset libai.tr
mftraining -F font_properties -U unicharset -O l libai.tr

8.Clustering产生字符形状正常化特征文件normproto

cntraining libai.tr

9.重命名文件

mv normproto libai.normproto
mv inttemp libai.inttemp
mv pffmtable libai.pffmtable
mv shapetable libai.shapetable
mv unicharset libai.unicharset

10.合并文件生成 traineddata 文件

➜  libai combine_tessdata libai.
Combining tessdata files
TessdataManager combined tesseract data files.
Offset for type  0 (libai.config                ) is -1
Offset for type  1 (libai.unicharset            ) is 140
Offset for type  2 (libai.unicharambigs         ) is -1
Offset for type  3 (libai.inttemp               ) is 855
Offset for type  4 (libai.pffmtable             ) is 140145
Offset for type  5 (libai.normproto             ) is 140274
Offset for type  6 (libai.punc-dawg             ) is -1
Offset for type  7 (libai.word-dawg             ) is -1
Offset for type  8 (libai.number-dawg           ) is -1
Offset for type  9 (libai.freq-dawg             ) is -1
Offset for type 10 (libai.fixed-length-dawgs    ) is -1
Offset for type 11 (libai.cube-unicharset       ) is -1
Offset for type 12 (libai.cube-word-dawg        ) is -1
Offset for type 13 (libai.shapetable            ) is 141781
Offset for type 14 (libai.bigram-dawg           ) is -1
Offset for type 15 (libai.unambig-dawg          ) is -1
Offset for type 16 (libai.params-model          ) is -1
Output libai.traineddata created successfully.

11.将文件拷贝到目标文件夹

cp libai.traineddata /usr/local/Cellar/tesseract/3.05.01/share/tessdata

12.再次识别

tesseract libai.jpg trained -l libai -psm 6

tesseract 能够进行 OCR 识别，但是对中文的支持不够好。所以如果要更好地识别中文字体需要进行大量的训练，有时间精力当然是一个方案，没有的话还是调用现成的 API 比较好。至少对印刷的中文字识别效果是很好的。
当然，如果有特殊需求用 tesseract 还是比较简单的方案。听说谷歌的 TensorFlow 能够通过机器学习达到很好的识别效果，不过看了看好像有点复杂啊。

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