How to Fine-tune EasyOCR to achieve better OCR performance

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

OCR is a valuable tool when you want to extract text from images. Sometimes, however, the OCR you are using is not working as well as you want it to for your specific needs. If you are facing such an issue, fine-tuning your OCR engine is the way to go. In this tutorial, I will show you how to fine-tune EasyOCR, a free, open-source OCR engine that you can use with Python.

Use OCR to read documents. Image made with DALL-E. OpenAI. (2023). ChatGPT [Large language model]. https://chat.openai.comPrerequisitesInstalling required packagesCloning required Git repositoryGenerating datasetConvert dataset to lmdb formatRetrieve a pre-trained OCR model:Run the fine-tuningRunning inference with your fine-tuned modelA qualitative test of performanceQuantitative test of performanceConclusionBasic Python knowledgeBasic knowledge of how to use the terminal

First off, let's install the required pip packages. I recommend making a virtual environment for this, though it is not required. Run the commands below one line at a time:

pip install firepip install lmdbpip install opencv-pythonpip install natsortpip install nltk

You also need to install PyTorch from this website (choose your specifications and copy the pip install command, see the command below that I used for my specifications). Preferably choose the GPU version, but the CPU version will… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI