A Comparative Study of Traditional OCR Models and Large Language Models for Optical Character Recognition

Both traditional OCR systems and LLM-based OCR approaches come with their own strengths and limitations. Traditional OCR engines such as EasyOCR and Tesseract are still highly relevant today because they provide reliable and cost-effective performance, particularly well-defined tasks and standardized document formats. They are especially useful in scenarios where local deployment is preferred; costs must remain predictable, and the input data is clean enough that heavy contextual reasoning is not required [6]. In such cases, traditional OCR can deliver strong accuracy with minimal complexity. On the other hand, Elam based OCR, especially multimodal models like Mistral-OCR represents a major step forward and document understanding. These models offer much greater flexibility when working with diverse, unstructured, or unfamiliar layouts. Their ability to use contextual understanding often allows them to perform better on complex, low quality or degraded documents. Additionally. They make OCR workflows easier to use through natural language prompting, reducing the need for rigid templates or manual rule-based extraction methods [4], [7]. However, LLM-based OCR also introduces important challenges. Users must account for risks such as hallucinations, where the model may generate text that appears believable but is not actually present in the document. Performance can also drop noticeably when image resolution is low, as highlighted in recent studies [2]. Other concerns include difficulty in consistently producing structured outputs without strict prompting and the lack of standardized confidence scores that traditional OCR systems often provide [4], [9]. Ultimately, the best OCR solution depends heavily on the specific use case. The key factors include the type of document (structured versus unstructured). The required accuracy level, Tolerance for different types of errors (misrecognition versus hallucination), cost constraints, volume of processing, latency requirements, and data privacy policies. Future research will likely focus on hybrid systems that combine traditional OCR reliability with LLM-level contextual understanding, along with better fine-tuning methods and stronger verification techniques to improve    trust and consistency in LLM-based OCR outputs.

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