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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition
| Date | Stars |
|---|---|
| 2026-07-24 | 289 |
| 2026-07-25 | 289 |
| 2026-07-28 | 289 |
| 2026-07-30 | 289 |
| 2026-08-06 | 289 |
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# Multi-Type-TD-TSR Check it out on <a href="https://colab.research.google.com/github/Psarpei/Multi_Type_TD_TSR/blob/main/Table_Recognition.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> Source Code of our Paper: [Multi-Type-TD-TSR](https://arxiv.org/abs/2105.11021) Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition # Description <img align="center" width="1000" height="" src="gifs/Table_Recognition_Pipeline.png"> ## Multi-Type-TD-TSR the Whole Pipeline <img align="right" width="390" height="" src="gifs/whole_pipeline.gif"> As global trends are shifting towards data-driven industries, the demand for automated algorithms that can convert digital images of scanned documents into machine readable information is rapidly growing. Besides the opportunity of data digitization for the application of data analytic tools, there is also a massive improvement towards automation of processes, which previously would require manual inspection of the documents. Although the introduction of optical character recognition (OCR) technologies mostly solved the task of converting human-readable characters from images into machine-readable characters, the task of extracting table semantics has been less focused on over the years. The recognition of tables consists of two main tasks, namely table detection and table structure recognition. Most prior work on this problem focuseson either task without offering an end-to-end solution or paying attention to real application conditions like rotated images or noise artefacts inside the document image. Recent work shows a clear trend towards deep learning approaches coupled with the use of transfer learning for the task of table structure recognition due to the lack of sufficiently large datasets. In this paper we present a multistage pipeline named Multi-Type-TD-TSR, which offers an end-to-end solution for the problem of table recognition. It utilizes state-of-the-art deep learning models for table detection and differentiates between 3 different types of tables based on the tables’ borders. For the table structure recognition we use a deterministic non-data driven algorithm, which works on all table types. We additionally present two algorithms. One for unbordered tables and one for bordered tables, which are the base of the used table structure recognition algorithm. We evaluate Multi-Type-TD-TSR on the ICDAR 2019 table structure recognition dataset and achieve a new state-of-the-art. ## Multi-Type-TD-TSR on Fully Bordered Tables <img align="left" width="390" height="" src="gifs/fully_bordered.gif"> For TSR on fully bordered tables, we use the erosion and dilation operation to extract the row-column grid cell image without any text or characters. The erosion kernels are generally thin vertical and horizontal strips that are longer than the overall font size but shorter than the size of the smallest grid cell and, in particular, must not be wider than the smallest table border width. Using these kernel size constraints results in the erosion operation removing all fonts and characters from the table while preserving the table borders. In order to restore the original line shape, the algorithm applies the dilation operation using the same kernel size on each of the two eroded images, producing an image with vertical and a second with horizontal lines. Finally, the algorithm combines both images by using a bit-wise ```or``` operation and re-inverting the pixel values to obtain a raster cell image. We then use the contours function on the grid-cell image to extract the bounding-boxes for every single grid cell. ## Multi-Type-TD-TSR on Unbordered Tables <img align="right" width="390" height="" src="gifs/unboardered.gif"> The TSR algorithm for unbordered tables works similarly to the one for bordered tables but utilizes the erosion operation in a different way. The erosion kern
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matched fp:5d7ce26c97a82151, topic:nlp, topic:natural-language-processing
matched fp:5d7ce26c97a82151, topic:ocr, readme:ocr
matched fp:5d7ce26c97a82151, topic:deep-learning
matched fp:5d7ce26c97a82151, topic:computer-vision