OCR vs VLM-OCR: Naive Benchmarking Accuracy for Scanned Documents
This article presents a naive but informative benchmark comparing traditional OCR technologies with Vision Language Models (VLMs) for processing scanned documents. Using the FUNSD dataset of noisy scanned forms, the study evaluates 10 different OCR solutions across multiple metrics including text similarity, word error rate, character error rate, and processing time. The results show VLMs (particularly Qwen and Mistral) significantly outperform traditional OCR methods in accuracy, especially for complex layouts and poor scan quality, though at the cost of longer processing times. The article provides practical recommendations for when to use each approach based on document complexity, volume requirements, and cost considerations. Complete benchmark code is available on GitHub for further exploration and improvement.

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