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When extracting structured data from repetitively organized documents, suchas dictionaries, directories, or even newspapers, a key challenge is tocorrectly segment what constitutes the basic text regions for the targetdatabase. Traditionally, such a problem was tackled as part of the layoutanalysis and was mostly based on visual clues for dividing (top-down)approaches. Some agglomerating (bottom-up) approaches started to considertextual information to link similar contents, but they required a properover-segmentation of fine-grained units. In this work, we propose a newpragmatic approach whose efficiency is demonstrated on 19th century FrenchTrade Directories. We propose to consider two sub-problems: coarse layoutdetection (text columns and reading order), which is assumed to be effectiveand not detailed here, and a fine-grained entry separation stage for which wepropose to adapt a state-of-the-art Named Entity Recognition (NER) approach. Byinjecting special visual tokens, coding, for instance, indentation or breaks,into the token stream of the language model used for NER purpose, we canleverage both textual and visual knowledge simultaneously. Code, data, resultsand models are available athttps://github.com/soduco/paper-entryseg-icdar23-code,https://huggingface.co/HueyNemud/ (icdar23-entrydetector* variants)

Entry Separation using a Mixed Visual and Textual Language Model: Application to 19th century French Trade Directories