Create README.md

README.md

@@ -1,31 +1,13 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: image
-    dtype: image
-  - name: latex
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 4170963766.954
-    num_examples: 87513
-  - name: validate
-    num_bytes: 229543753.0
-    num_examples: 5000
-  - name: test
-    num_bytes: 222101522.0
-    num_examples: 5000
-  download_size: 3779553359
-  dataset_size: 4622609041.954
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validate
-    path: data/validate-*
-  - split: test
-    path: data/test-*
+license: bsd-3-clause-clear
+task_categories:
+- image-to-text
 ---
+## LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement
+
+Tab2Latex: a Latex table recognition dataset, with 87,513 training, 5,000 validation, and 5,000 test instances. The LaTeX sources are collected from academic papers within these six distinct sub-fields of computer science—Artificial Intelligence, Computation and Language, Computer Vision and Pattern Recognition, Cryptography and Security, Programming Languages, and Software Engineering—from the arXiv repository, covering the years 2018 to 2023. Once the paper sources are downloaded, tables are identified and extracted from the LaTeX source code by matching \begin{tabular} and \end{tabular} and removing the comments. Then, the LaTeX table source scripts are rendered to PDF format and converted to PNG format at 160 dpi.
+
+### Data Fields
+* id: instance id
+* image: the rendered image (PIL.Image) from the Latex source code
+* latex: the Latex source code for the table