ssd_keras
layout-parser
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| ssd_keras | layout-parser | |
|---|---|---|
| 4 | 6 | |
| 1,846 | 4,453 | |
| - | 3.6% | |
| 0.0 | 0.0 | |
| about 2 years ago | about 2 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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ssd_keras
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Failed to get convolution algorithm. This is probably because cuDNN failed to initialize,
In Tensorflow/ Keras when running the code from https://github.com/pierluigiferrari/ssd_keras, use the estimator: ssd300_evaluation. I received this error.
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Shared weights between different implementations
Yeah, the order of axes was different between those 2. Another guy used https://github.com/pierluigiferrari/ssd_keras https://github.com/uhfband/keras2caffe/blob/master/keras2caffe/convert.py probably not much actual use but maybe some more reassurance?
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Simplest way to deploy Keras NN model into C++?
Don't know about simplest, but we either used caffe or tensorrt, it is maybe a bit difficult to use but I'd actually say simple fast GPU inference is what it's geared towards. There is a keras -> caffe converter https://github.com/pierluigiferrari/ssd_keras here, I think. Caffe is a c++ lib, typical, with dependencies and all. I've never heard anything of tensorflow running on c++. But with tensorrt you should get an "artifact" that you'd load, no matter where it comes from
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ValueError: Layer model expects 1 input(s), but it received 2 input tensors. Help?
Tensorflow V1 Keras code (original repo): Github Repo
layout-parser
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Crates for converting PDF's into Markdown
I built my own solution using a combination of Tesseract and OpenCV (in python). But even though the source PDF content is computer generated, I still get sporadic OCR errors. After writing my solution, I came across this https://github.com/Layout-Parser/layout-parser which might be a better starting point for dealing with PDFs but I haven't tried it yet.
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OCR help required
This sound more like a layout parking issue. Look at Layout Parser, it has helped me on many occasions when I was battling to extract info from PDF documents.
- Amateur programmer here. Will Rust be used in backend for software in the future?
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Extract text from PDF
One of the tools I'm excited about (but haven't used in production) is LayoutParser. It's open-source, and can do some document image analysis especially on non-generic docs.
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Document Classification
One project that I saw not to long ago which might be useful is this: https://github.com/Layout-Parser/layout-parser
- A Python Library for Document Layout Understanding
What are some alternatives?
cppflow - Run TensorFlow models in C++ without installation and without Bazel
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
zero-shot-object-tracking - Object tracking implemented with the Roboflow Inference API, DeepSort, and OpenAI CLIP.
py-pdf-parser - A Python tool to help extracting information from structured PDFs.
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
tika-python - Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
BCNet - Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]
a-PyTorch-Tutorial-to-Object-Detection - SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection
simpletransformers - Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
SSD-pytorch - SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity
shabby-pages - ShabbyPages is a state-of-the-art corpus of born-digital document images with both ground truth and distorted versions appropriate for use in training models to reverse distortions and recover to original denoised documents.