From 750941374ef63b20a28a0a10773fae0e77aea9df Mon Sep 17 00:00:00 2001 From: Jan <116908874+jk4e@users.noreply.github.com> Date: Tue, 18 Mar 2025 01:15:00 +0100 Subject: [PATCH] Fix typo (#14884) --- docs/ppstructure/overview.en.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/ppstructure/overview.en.md b/docs/ppstructure/overview.en.md index d1e3f5471..2e38a94ff 100644 --- a/docs/ppstructure/overview.en.md +++ b/docs/ppstructure/overview.en.md @@ -32,7 +32,7 @@ The main features of PP-StructureV2 are as follows: - Support layout analysis of documents in the form of images/pdfs, which can be divided into areas such as **text, titles, tables, figures, formulas, etc.**; - Support common Chinese and English **table detection** tasks; - Support structured table recognition, and output the final result to **Excel file**; -- Support multimodal-based Key Information Extraction (KIE) tasks - **Semantic Entity Recognition** (SER) and **Relation Extraction (RE); +- Support multimodal-based Key Information Extraction (KIE) tasks - **Semantic Entity Recognition** (SER) and **Relation Extraction** (RE); - Support **layout recovery**, that is, restore the document in word or pdf format with the same layout as the original image; - Support customized training and multiple inference deployment methods such as python whl package quick start; - Connect with the semi-automatic data labeling tool PPOCRLabel, which supports the labeling of layout analysis, table recognition, and SER. @@ -49,7 +49,7 @@ The figure shows the pipeline of layout analysis + table recognition. The image ### 3.1.1 Layout recognition returns the coordinates of a single word -The following figure shows the result of layout analysis on single word， please refer to the [doc](./blog/return_word_pos.en.md). +The following figure shows the result of layout analysis on single word，please refer to the [doc](./blog/return_word_pos.en.md). ![show_0_mdf_v2](./images/799450d4-d2c5-4b61-b490-e160dc0f515c.jpeg)