- Multi-Type-TD-TSR VS donut
- Multi-Type-TD-TSR VS MetalTranslate
- Multi-Type-TD-TSR VS Recognition-of-logical-document-structures
- Multi-Type-TD-TSR VS oemer
- Multi-Type-TD-TSR VS CascadeTabNet
- Multi-Type-TD-TSR VS deathcounter_ocr
- Multi-Type-TD-TSR VS Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
- Multi-Type-TD-TSR VS elastic_transformers
- Multi-Type-TD-TSR VS 100DaysOfML
- Multi-Type-TD-TSR VS CodeSearchNet
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Multi-Type-TD-TSR reviews and mentions
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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.
Stats
Psarpei/Multi-Type-TD-TSR is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of Multi-Type-TD-TSR is Jupyter Notebook.