Best OCR Models for Text Recognition in Images

Optical character recognition (OCR) technology, which enables machines to interpret text from images, finds its primary use in document-related applications but is expanding to non-document scenarios like license plates and logos, known as "focused scene OCR." A detailed analysis of 25 OCR models, including both local and cloud-based options such as OpenAI's GPT-4, Google's Gemini, and open-source models like EasyOCR, was conducted across various industrial use cases, assessing their performance in terms of accuracy, speed, and cost. The study found that multimodal vision language models (VLMs) generally performed well, with OpenAI's GPT-4.5 Preview achieving the highest accuracy, while local models like EasyOCR excelled in cost-efficiency. The research emphasized the importance of speed and cost metrics, introducing "speed efficiency" and "cost efficiency" as measures of a model's practicality. EasyOCR emerged as the most economically efficient solution, maintaining accuracy while being cost-effective, whereas Anthropic’s Claude 3 Opus and Google’s Gemini Pro 1.0 showed superior performance in accuracy and speed efficiency, respectively.