You could train a character-level language model, e.g. an LSTM, on the real short texts, and use the perplexity as the signal to know whether a piece of text is real or not.
In order to find an appropriate perplexity threshold, you can have a look at the distribution of perplexities over a validation holdout dataset.
What you are describing is one of the "standard" NLP problems faced in NLP and it usually referred to as "natural language inference" (NLI), or sometimes also as "textual entailment".
There is plenty of research in this kind of task, and its variants, like cross-lingual NLI (XNLI). I suggest you have a look at nlpprogress (link) ...
The Python Library
Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and “read” the text embedded in images. Python-tesseract is a wrapper for Google's Tesseract-OCR Engin
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