EasyOCR vs Tesseract OCR
EasyOCR
github.comTesseract OCR
tesseract-ocr.github.ioSide 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 |
| iOS | — | Yes |
| Android | — | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · OCR API Software | 8.4 (#7) | 8.2 (#15) |
| Input formats | Images can be supplied as a file path, NumPy array, bytes, or URL. | It supports image formats including PNG, JPEG and TIFF. |
| Integrations | The repository says EasyOCR is integrated into Hugging Face Spaces using Gradio and provides a web demo. | The project documentation lists language wrappers including Python, Java, Swift, Flutter, Ruby, Rust, and Go. |
| Languages | The project states that it supports more than 80 languages, and compatible languages can be read together. | The project says Tesseract can recognize more than 100 languages out of the box. |
| License | The public GitHub repository identifies EasyOCR as licensed under Apache-2.0. | Tesseract is distributed under the Apache License 2.0. |
| Notable limitation | The README lists handwritten text support as planned work. | The project does not include a GUI application, and the README says image quality may need improvement for better OCR results. |
| Purpose | EasyOCR is a Python module for extracting text from images, including natural scene text and dense document text. | Tesseract is an open source text recognition (OCR) engine 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 project directs users to its documentation and FAQ, forums, past issues and mailing lists, and asks that repository issues be used for bugs 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 engine that extracts printed text from images. It can be run from the command line or used through an API; the project does not include a graphical application. It recognizes more than 100 languages out of the box and supports Unicode UTF-8. Version 4 introduced an LSTM neural-network engine for line recognition while retaining the legacy character-pattern engine. Input formats include PNG, JPEG, TIFF, JPEG 2000, GIF, WebP, BMP, and PNM. Output formats include plain text, hOCR, PDF, TSV, ALTO, and PAGE. Tesseract cannot read PDF files directly, although PDF is available as an output format; the documentation suggests converting files or using OCRmyPDF. The project documents wrappers for several languages, including Python, Java, Swift, Flutter, Ruby, Rust, and Go, as well as C and C++ APIs. The repository code uses Apache License 2.0, though dependencies may have different licenses. Image quality may need improvement for better recognition results.