To answer your question, let’s do a little experiment. Some sections you can expand.

##
gpt-4-vision text extraction at default temperature

The image contains text and a series of mathematical formulas. Here’s the verbatim text and the formulas converted to Unicode characters as accurately as possible:

symbols present within a frame, allowing the symbol with 𝑁𝑠𝑦𝑛𝑐 on the variance of 𝛽̂𝐶 is given by the Cramér-Rao lower bound for the frequency synchronization estimated. The coherent carrier signal samples, 𝛽̂𝐶 is the underlying coherent

𝑁𝑠𝑦𝑛𝑐 contiguous samples exposing the true unknown

phase and amplitude [23]:

𝑎𝑟𝑣𝑎𝑟(𝛽̂𝐶) ≥ 𝜎2𝐸𝐹𝑅2/(𝐸𝑅2𝑁𝑠𝑦𝑛𝑐(1−𝛾𝑠𝑦𝑛𝑐))

Based on the above expression, the 𝑁𝑠𝑦𝑛𝑐 can be approxi-

mated as

𝑁𝑠𝑦𝑛𝑐 = 𝐸𝑁𝑅/𝜎2𝐸𝑅.

The 𝑁𝑠𝑦𝑛𝑐 can be determined by limiting the variance of the phase error to the Cramér-Rao lower bound on the variance of 𝛽̂𝐶:

𝑎𝑟𝑣𝑎𝑟(𝛽̂𝐶) ≤ 𝜎2𝐸𝑅2/(𝐸𝑅2𝑁𝑠𝑦𝑛𝑐(1−𝛾𝑠𝑦𝑛𝑐)) (15)

Based on this

𝑁𝑠𝑦𝑛𝑐 = 𝟔𝐄𝑅2𝑁𝑠𝑦𝑛𝑐/(𝐄𝐑2𝑁𝑠𝑦𝑛𝑐 (1−𝛾𝑠𝑦𝑛𝑐)) ≈ 𝟔𝐄𝑅2𝑁/(𝐄𝐑2.𝟔𝑁𝑠𝑦𝑛𝑐) (16)

Suppose 𝑁𝑠𝑦𝑛𝑐 = 2𝑥10 and 𝑁 = 10 dBc. Since deterministic 𝜎𝐸𝑅 deserves to wish to minimize 𝜎𝑦𝑛𝑠𝑦𝑛𝑐, 𝑁 must satisfy

𝑁 ≤ 𝟓𝐄𝐑2𝑁𝑠𝑦𝑛𝑐/𝜎2𝐄𝑅.

and 𝜎𝑦𝑛𝑠𝑦𝑛𝑐 should be chosen to not carry information. For another practical tradeoff on 𝑁 is that the transmitter of two efficient IFFT and FFT operations it must be a power of 𝑁 = 2𝑣. No FFT on from 𝑁 of which are apparent

deviates from this norm.

By combining the power-of-two constraint with the reasonable values of 𝑁 satisfying (16), one can construct 𝑆 as

𝑆 = {𝑞2𝑣 : 𝑞 ∈ [2, 9], 𝑣 ≤ 12}

##
gpt-4-vision text extraction at temperature=2.0

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You can see that a very high temperature turns the character recognition into complete nonsense.

Conclusion: temperature and token selection affects the AI replay of text from an image. Remember, it is still a chat AI with token sampling.

Your solution: Decrease temperature or top-p API parameters to a very low number such as 0.001.