Multi-Type-TD-TSR
CascadeTabNet
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| Multi-Type-TD-TSR | CascadeTabNet | |
|---|---|---|
| 4 | 1 | |
| 236 | 1,397 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | over 2 years ago | |
| Jupyter Notebook | Python | |
| MIT License | MIT License |
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Multi-Type-TD-TSR
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[D] Getting super-level table extraction
Recently, I've been researching extracting tables from image documents. First I tried with pdfs, however, the data extraction libraries like camelot are inconsistent. I found a deep learning model called CascadeTabNet. The detection results are okay but cell recognition is poor. I even found Multi-Type-TD-TSR for table extraction. It uses image processing techniques to find the grids. It performs well on structured and bordered tables. However, it messes up if the cell is not properly aligned. Even if extraction is successful, aggregation of multi-line cells, i.e post-processing, is not very obvious.
- Multi-Type-TD-TSR - Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition (State of the art approach for table structure recognition published on KI2021 - 44th German Conference on Artificial Intelligence)
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Multi-Type-TD-TSR - Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table Representations
Check it out on my Github: https://github.com/Psarpei/Multi-Type-TD-TSR
- Multi-Type-TD-TSR - Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table Representations (New state-of-the-art approach for table structure recognition)
CascadeTabNet
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[D] Getting super-level table extraction
Recently, I've been researching extracting tables from image documents. First I tried with pdfs, however, the data extraction libraries like camelot are inconsistent. I found a deep learning model called CascadeTabNet. The detection results are okay but cell recognition is poor. I even found Multi-Type-TD-TSR for table extraction. It uses image processing techniques to find the grids. It performs well on structured and bordered tables. However, it messes up if the cell is not properly aligned. Even if extraction is successful, aggregation of multi-line cells, i.e post-processing, is not very obvious.
What are some alternatives?
donut - Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022
deepdoctection - A Repo For Document AI
MetalTranslate - Customizable machine translation in C++
table-transformer - Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images). This is also the official repository for the PubTables-1M dataset and GriTS evaluation metric.
Recognition-of-logical-document-structures - First approach for recognizing logical document structures like texts, sentences, segments, words, chars and sentence/segment depth based on recurrent neural network grammars.
oemer - End-to-end Optical Music Recognition (OMR) system. Transcribe phone-taken music sheet image into MusicXML, which can be edited and converted to MIDI.
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
ITC - Computer Science coursework and projects at Tec de Monterrey 👨🎓
elastic_transformers - Making BERT stretchy. Semantic Elasticsearch with Sentence Transformers
deathcounter_ocr - A python script which detects death messages by using OCR and displays a corrosponding deathcounter. Preconfigured for Elden Ring
docutron - Docutron Toolkit: detection and segmentation analysis for legal data extraction over documents.