hnmspirit / table_datagen

Data Generation for Table Recognition

Rethinking Table Parsing using Graph Neural Networks

• original paper: https://arxiv.org/abs/1905.13391
• original repo: https://github.com/hassan-mahmood/TIES_DataGeneration

Dataset Generation

• TableGeneration: Contains functionality to generate tables
• TFGeneration: Contains functionality to generate tfrecord files. It uses TableGeneration module to generate tables.
• generate_data: Main script to start dataset generation
• unlv_distribution: a binary file that contains words distribution of UNLV dataset (types of words: numbers, alphabets and words containing special characters)

Installing

• download firefox geckodriver

Generate tfrecords

• bash run.sh

You can download UNLV dataset from this link: https://drive.google.com/drive/folders/1yES8Se8pyGsvLt92dJFz7z7AJQHjt4GA?usp=sharing

Table Generation:

All of the content used in generated tables is extracted from UNLV dataset. We also extracted the distribution of alphabetical words, numbers and special character words from it and used same distribution for our dataset.

Based on the distribution of words, we generated tables of 4 categories(as mentioned in the paper). The code for generating 4 categories of tables is different than a simple "generate 4 categories" approach.

A table is generated in multiple steps like a lego building block(with each step contributing to generation of table):

1. The data types of columns are defined e.g. which column will contain alphabets, numbers or special character words
2. Some cells are randomly selected for missing data
3. Rows and Column spans are added to table
4. The table can be categorized into two ways based on headers(both categories are equally likely to be chosen):
   • Table with regular headers(Table with only first row containing headers.)
   • Table with irregular headers(Table with headers in first row and first column. This category can have multiple row spans for headers of first column.)
5. Table borders are chosen randomly. We define border_categories with 4 possibilities(all four categories are equally likely to be chosen):
   • All borders
   • No borders
   • Borders only under headings
   • Only internal borders
6. An equivalent HTML code is generated for this table.
7. This HTML coded table is converted to image using selenium.
8. Finally, shear and rotation transformations are applied to the table image.

TFGeneration

During table generation process, the words are assigned unique IDs. During html-to-image conversion, these words are localized with bounding boxes and transformed on image transformation accordingly.

Based on these words IDs, we compute 3 adjacency matrices: same_row: if two IDs are sharing a row, the value corresponding to that location will be 1 in that matrix. same_column: Matrix to show which IDs are sharing column same_cell: Matrix to show which IDs are sharing cell

Instead of just storing the image, we also store some metadata:

1. image height and width
2. number of words in table
3. table category
4. word IDs
5. adjacency matrix for same cells, same rows and same columns