  Text spotting has seen tremendous progress in recent years yielding
performant techniques which can extract text at the character, word or line
level. However, extracting blocks of text from images (block-level text
spotting) is relatively unexplored. Blocks contain more context than individual
lines, words or characters and so block-level text spotting would enhance
downstream applications, such as translation, which benefit from added context.
We propose a novel method, BTS-LLM (Block-level Text Spotting with LLMs), to
identify text at the block level. BTS-LLM has three parts: 1) detecting and
recognizing text at the line level, 2) grouping lines into blocks and 3)
finding the best order of lines within a block using a large language model
(LLM). We aim to exploit the strong semantic knowledge in LLMs for accurate
block-level text spotting. Consequently if the text spotted is semantically
meaningful but has been corrupted during text recognition, the LLM is also able
to rectify mistakes in the text and produce a reconstruction of it.
