Easter2
Multi-Type-TD-TSR
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| Easter2 | Multi-Type-TD-TSR | |
|---|---|---|
| 1 | 4 | |
| 73 | 236 | |
| - | - | |
| 0.0 | 0.0 | |
| about 1 year ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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Easter2
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Researchers Propose Easter2.0, a Novel Convolutional Neural Network CNN-Based Architecture for the Task of End-to-End Handwritten Text Line Recognition that Utilizes Only 1D Convolutions
Continue reading | Checkout the paper and github link
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)
What are some alternatives?
Handwritten-Text-Recognition - As part of the project we examine several approaches for recognizing text in images and predicting the whole digital text. All aproaches follow the method to break the image down into the smaller parts like lines, words or characters.
donut - Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022
kraken - OCR engine for all the languages
MetalTranslate - Customizable machine translation in C++
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.
CascadeTabNet - This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"
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.