OCR Accuracy Showdown: PaperLab vs. LlamaIndex

When it comes to document digitization, Optical Character Recognition (OCR) accuracy is everything. One misplaced character can completely alter the meaning of complex data, especially in scientific or mathematical contexts.

At PaperLab, we recently ran a simple experiment to see how our OCR accuracy compares with LlamaIndex, focusing specifically on how both tools convert research content into Markdown format

LlamaIndex vs Paperlab Markdown output

Methodology

A sample table was taken from the research paper: https://www.researchgate.net/publication/395423940_Reparameterized_slashed_lognormal_regression_model_Diagnostics_and_application_to_mineral_data

What we did in simple steps:

1. Input: Uploaded the same table image to both platforms.

   
   

2. Output: Generated Markdown versions from each tool.

   
   

3. Verification: Opened both outputs in VS Code to visually inspect Markdown accuracy.

   
   

4. Error Calculation: Compared each output against the original table to measure the error rate.

   
   

Since LlamaIndex produced its Markdown output in LaTeX, we used Overleaf to convert it into readable math expressions for verification.

Results

The results were shocking:

Platform	Error Rate	Interpretation
LlamaIndex (Agentic Plus Version)	44.6%	Misread equations, changed meanings, and even had a ‘Parse Error’ during Markdown parsing
PaperLab	0%	Matched the original table character for character

What We Found

The biggest surprise was that LlamaIndex’s Markdown file could not fully parse, showing a ‘Parse Error’  that indicated it failed to handle the structure of the source material.

Parse error in markdown

Even after conversion, the math equations were misread and altered in ways that could have completely changed the interpretation of the research data.

PaperLab, in contrast, produced a clean, accurate Markdown file that perfectly preserved every equation and symbol from the original.

Why This Matters

OCR accuracy is not just about getting the words right. In research, data analysis, or technical writing, one incorrect symbol or decimal can change an entire finding. This small experiment highlights how important it is to choose your OCR platform carefully.