How To Understand OCR Quality To Optimize Performance

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

OCR is an important tool for understanding documents, which it does by extracting all text from an image, which can then be combined with models like LLMs to create powerful AI systems. Despite current OCRs performing well, they are not perfect and you need tools to measure the quality of your OCR. Receiving feedback on the quality of your OCR can then be used to optimize your OCR effectively, for example, by implementing pre-processing steps like thresholding.

Learn more about OCR quality in this article. OpenAI. (2024). ChatGPT [Large language model]. https://chat.openai.comMotivationTechniques to optimize your OCRHow I tested the pre-processing stage for my OCR engineConclusion

The motivation for this article is that I am currently working on a project extracting text from receipts. I then want to use this OCR combined with Llama2 for information extraction and answering from the given receipt. My problem, however, is that the quality of the OCR is essential for the pipeline, as the Llama2 model quality depends on the OCR quality, as one usually says within machine learning: trash in, gives trash out. To have an iterative process where I could tune my OCR, I needed to see what made the model perform better, which required… Read the full blog for free on Medium.

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Published via Towards AI