EasyOCR vs Tesseract OCR
EasyOCR
github.comTesseract OCR
github.comSide by side
| Figure | EasyOCR | Tesseract OCR |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| Android | — | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · OCR software | 8.4 (#13) | 9.1 (#2) |
| Free plan | Yes | Yes |
| Handwriting OCR | No | No |
| Input formats | Images can be supplied as a file path, NumPy array, bytes, or URL. | The project lists PNG, JPEG, and TIFF among its supported image formats. |
| Supported inputs | image | image |
| Installation | The maker documents installation through pip, from source, or with Docker; Windows users may need to install PyTorch manually first. | The documentation describes installation through Linux distributions, macOS package managers, Windows installers, and source builds. |
| Languages | The project states that it supports more than 80 languages, and compatible languages can be read together. | The project says Tesseract supports UTF-8 and recognizes more than 100 languages out of the box. |
| License | The public GitHub repository identifies EasyOCR as licensed under Apache-2.0. | The code is licensed under Apache License 2.0 and is provided without warranties under the license terms. |
| Primary platform | desktop | desktop |
| Purpose | EasyOCR is a Python module for extracting text from images, including natural scene text and dense document text. | Tesseract is an OCR engine and command-line program for extracting printed text from images. |
| Support | The repository directs enterprise inquiries to Jaided AI for custom OCR/AI implementation, training or fine-tuning, and deployment services. | The README directs users to documentation, FAQs, user and developer forums, past issues, and mailing lists, and says issues should be used for bug reports rather than questions. |
Plans
In their own words
EasyOCR
EasyOCR is a Python module that extracts text from images, including natural scene text and dense document text. The project says it supports more than 80 languages, and compatible languages can be processed together. Its readtext method can return recognized text with bounding-box coordinates and confidence values, or provide text alone. Inputs can be image paths, NumPy arrays, bytes, or URLs. The Reader can use a GPU when available or run in CPU-only mode. Pretrained models download automatically during use, with manual downloads available for offline environments. The API also allows custom recognition networks and settings for detection, recognition, decoding, and text-box merging. Installation options include pip, source, and Docker; Windows users may need to install PyTorch manually first. A web demo is available, and the repository says EasyOCR is integrated into Hugging Face Spaces through Gradio. The public repository identifies the project as Apache-2.0 licensed. There is no paid plan listed. Handwriting recognition is not supported; the README lists it as planned work.
Tesseract OCR
Tesseract OCR is a free, open-source OCR engine and command-line program for extracting printed text from images. It accepts image inputs including PNG, JPEG, and TIFF, and can output plain text, hOCR, PDF, TSV, ALTO, and PAGE formats. PDF options include searchable PDFs and invisible-text-only PDFs. Tesseract 4 and later include an LSTM-based engine focused on line recognition, while Legacy OCR Engine mode retains compatibility with Tesseract 3. The project says it recognizes more than 100 languages out of the box; language data is installed separately from the engine. Developers can build applications with the C or C++ libtesseract API, and the documentation lists wrappers for other languages. The repository code uses the Apache License 2.0. Tesseract does not include a graphical user interface and points users to third-party interfaces. It uses Leptonica to open images, and improving input image quality may be needed for better recognition results. Installation instructions cover Linux, macOS, and Windows, with listed platforms also including Android and self-hosting.