Update README.md

README.md

@@ -12,7 +12,7 @@ tags:
 We present the **Arxiv Figures & Tables Database (AFTdb)**, which consists of an aggregation of figures and tables from scientific articles sourced from the arXiv platform.
 
 The purpose of this dataset is to train multimodal
-models specialized in images of document-type objects (graphs, functional diagrams, tables, etc.), rather than photographic-type images. The idea is that a model trained on this type of data will be more coherent within the context of document corpora than a model trained on pictorial compositions. To establish a connection between the two modalities (image and text), captions for each object are also provided. As captions can sometimes be very brief, the article's summary is also included to add context to the document object if necessary.
+models specialized in images of document-type objects (graphs, functional diagrams, tables, etc.), rather than photographic-type images. The idea is that a model trained on this type of data will be more coherent within the context of document corpora than a model trained on pictorial compositions. To establish a connection between the two modalities (image and text), captions for each object are also provided. As captions can sometimes be very brief, the article's summary is also included to add context to the document object if necessary. All textual data (titles, abstracts, and captions) are available in both English (original language) and French through translation using Google Translate.
 
 For this reason, a corpus of scientific articles was prioritized. Due to the scientific rigor demanded, each document-type object is systematically accompanied by a caption (similar to captions for pictorial images on platforms like Flickr, for example).