diff --git a/doc/doc_en/dataset/kie_datasets_en.md b/doc/doc_en/dataset/kie_datasets_en.md index 7b476f77d0..ff839097c5 100644 --- a/doc/doc_en/dataset/kie_datasets_en.md +++ b/doc/doc_en/dataset/kie_datasets_en.md @@ -9,7 +9,7 @@ Here are the common datasets key information extraction, which are being updated #### 1. FUNSD dataset - **Data source**: https://guillaumejaume.github.io/FUNSD/ -- **Data Introduction**: The FUNSD dataset is a dataset for form comprehension. It contains 199 real, fully annotated scanned images, including market reports, advertisements, and academic reports, etc., and is divided into 149 50 training sets and 50 test sets. The FUNSD dataset is suitable for many types of DocVQA tasks, such as field-level entity classification, field-level entity connection, etc. Part of the image and the annotation box visualization are shown below: +- **Data Introduction**: The FUNSD dataset is a dataset for form comprehension. It contains 199 real, fully annotated scanned images, including market reports, advertisements, and academic reports, etc., and is divided into 149 training set and 50 test set. The FUNSD dataset is suitable for many types of DocVQA tasks, such as field-level entity classification, field-level entity connection, etc. Part of the image and the annotation box visualization are shown below:
@@ -35,7 +35,7 @@ Here are the common datasets key information extraction, which are being updated ## 3. wildreceipt dataset - **Data source**: https://arxiv.org/abs/2103.14470 -- **Data introduction**: XFUND is an English receipt dataset, which contains 26 different categories. There are 1267 training images and 472 evaluation images, in which 50,000 textlines and boxes are annotated. Part of the image and the annotation box visualization are shown below. +- **Data introduction**: wildreceipt is an English receipt dataset, which contains 26 different categories. There are 1267 training images and 472 evaluation images, in which 50,000 textlines and boxes are annotated. Part of the image and the annotation box visualization are shown below.