tesserocr
A simple, Pillow-friendly, Python wrapper around tesseract-ocr API using Cython
Decision gist · record as of 2026-08-14
Yes, if you need OCR and can install system libraries. tesserocr is actively maintained, production-stable, has no known vulnerabilities, and offers genuine concurrency advantages over pure-Python OCR libraries. The medium install friction (external C++ dependencies) is the main trade-off; Windows users should use Conda or pre-built wheels to avoid compilation.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires libtesseract (>=3.04) and libleptonica (>=1.71) libraries installed on the system; on Linux/Ubuntu install via apt-get, on macOS via Homebrew, or use Conda on Windows.
- Medium install friction due to compiled C++ dependencies.
- Requires libtesseract (>=3.04) and libleptonica (>=1.71) to be installed separately on Linux/macOS, or pre-packaged wheels on Windows.
License · maintenance · safety
MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-08-04 (10 days) · last repo commit 2026-08-04 · 2,171 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 562,527 downloads/mo, #5,988 on PyPI
Alternatives
Verify before relying
pip install tesserocr
from tesserocr import PyTessBaseAPI
with PyTessBaseAPI() as api:
api.SetImageFile('sample.jpg')
print(api.GetUTF8Text())- Whether traineddata files are bundled or must be downloaded separately for language support
- Performance characteristics when processing large batches of images
- Compatibility with PyPy implementation beyond CPython
What it is and what it does
tesserocr is a Python wrapper around the Tesseract OCR engine, built with Cython to provide direct access to Tesseract's C++ API. It extracts text and metadata from images, supporting both file paths and Pillow Image objects. The package is designed for concurrent use with Python's threading module, releasing the GIL during image processing to enable true parallel execution.
The library exposes both high-level convenience functions (like `image_to_text()` and `file_to_text()`) and a lower-level API (`PyTessBaseAPI`) for advanced use cases such as component detection, orientation/script detection, and per-symbol confidence scores. It requires system libraries libtesseract and libleptonica to be installed separately on Unix-like systems, though Windows wheels bundle these dependencies.
Use it for
- Batch processing scanned documents or photographs to extract searchable text for archival systems
- Building concurrent image-to-text pipelines that leverage threading for high-throughput OCR workflows
- Detecting document orientation and script language before applying language-specific OCR models
- Extracting text regions with per-character confidence scores for quality control in document processing
- Integrating OCR into data pipelines that already use Pillow for image manipulation
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need OCR and can install system libraries.
tesserocr is actively maintained, production-stable, has no known vulnerabilities, and offers genuine concurrency advantages over pure-Python OCR libraries. The medium install friction (external C++ dependencies) is the main trade-off; Windows users should use Conda or pre-built wheels to avoid compilation.
Install
tesserocr on PyPI
Before you install
Medium install friction due to compiled C++ dependencies. Requires libtesseract (>=3.04) and libleptonica (>=1.71) to be installed separately on Linux/macOS, or pre-packaged wheels on Windows. Package is actively maintained with recent releases and builds available for Python 3.9–3.14 across multiple platforms.
Requires libtesseract (>=3.04) and libleptonica (>=1.71) libraries installed on the system; on Linux/Ubuntu install via apt-get, on macOS via Homebrew, or use Conda on Windows.
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install tesserocr
from tesserocr import PyTessBaseAPI
with PyTessBaseAPI() as api:
api.SetImageFile('sample.jpg')
print(api.GetUTF8Text())Verify before relying
- Whether traineddata files are bundled or must be downloaded separately for language support
- Performance characteristics when processing large batches of images
- Compatibility with PyPy implementation beyond CPython
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagecysignals |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 562,527 / month, #5,988 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersOperating System :: POSIXProgramming Language :: CythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Multimedia :: Graphics :: Capture :: ScannersTopic :: Multimedia :: Graphics :: Graphics ConversionTopic :: Scientific/Engineering :: Image Recognition |
Evidence: tesserocr-2.11.0-cp310-cp310-macosx_15_0_arm64.whl; tesserocr-2.11.0-cp310-cp310-macosx_15_0_x86_64.whl; tesserocr-2.11.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; tesserocr-2.11.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; tesserocr-2.11.0-cp310-cp310-musllinux_1_2_x86_64.whl; tesserocr-2.11.0-cp311-cp311-macosx_15_0_arm64.whl; tesserocr-2.11.0-cp311-cp311-macosx_15_0_x86_64.whl; tesserocr-2.11.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; tesserocr-2.11.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; tesserocr-2.11.0-cp311-cp311-musllinux_1_2_x86_64.whl; tesserocr-2.11.0-cp312-cp312-macosx_15_0_arm64.whl; tesserocr-2.11.0-cp312-cp312-macosx_15_0_x86_64.whl; tesserocr-2.11.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; tesserocr-2.11.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; tesserocr-2.11.0-cp312-cp312-musllinux_1_2_x86_64.whl; tesserocr-2.11.0-cp313-cp313-macosx_15_0_arm64.whl; tesserocr-2.11.0-cp313-cp313-macosx_15_0_x86_64.whl; tesserocr-2.11.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; tesserocr-2.11.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; tesserocr-2.11.0-cp313-cp313-musllinux_1_2_x86_64.whl
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See also pytesseract · unstructured.pytesseract · ocrmypdf · pyocr · keras-ocr · ocrmac · perceptron · easyocr · kreuzberg · ManimPango