kuasar
keras-ocr
| kuasar | keras-ocr | |
|---|---|---|
| 5 | 4 | |
| 1,273 | 1,405 | |
| 1.3% | - | |
| 8.4 | 1.4 | |
| 28 days ago | 4 months ago | |
| Rust | Python | |
| Apache License 2.0 | MIT License |
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kuasar
- My VM is lighter (and safer) than your container
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The advantage of WASM compared with container runtimes
Right now most early examples alas boot a container with a wasm runtime for each wasm instance, which is a sad waste. The whole advantage of wasm should be very lightweight low overhead wasm runtime instances atop a common wasm process. Having a process or container for each instance loses a ton of the benefit, makes it not much better than a regular container.
Thankfully there is work like the Containerd Sandbox API which enables new architectures like this. https://github.com/containerd/containerd/issues/4131
It's still being used to spawn a wasm processes per instance for now, but container runtime project Kuasar is already using the Sandbox API to save significant resources, and has already chimed in in comments on HN to express a desire to have shared-process/multi-wasm-instamxe runtimes, which could indeed allow sub ms spawning that could enable instance per request architectures. https://github.com/kuasar-io/kuasar
- FLaNK Stack Weekly for 30 April 2023
- Kuasar - A Container Runtime in Rust
- Kuasar: An efficient multi-sandbox container runtime
keras-ocr
- FLaNK Stack Weekly for 30 April 2023
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Ask HN: Best pretrained OCR model for dashcam footage?
I'm trying to detect things like speed limits, stop signs, retail building signs from a relatively low quality dashcam. The video is 1440p, but the optics aren't great.
So far I've been using generic OCR models like [1] and [2], but the results aren't great.
[1] https://github.com/faustomorales/keras-ocr
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Why do new architectures still use old models?
Yes, you should be able to do it by replacing the the backbone and training the other parts again. The results may be better or worse than you expected. See: https://github.com/faustomorales/keras-ocr/issues/113
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How easy would it be/how would I go about implementing automated OCR + word count estimate after a file upload for a translation website?
There's plenty of OCR models around. If you'll have a Python server handling this, you can try keras-ocr. If you want to do this right in the browser, you can use a tflite model.
What are some alternatives?
pandas-ai - Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
agorakube - Agorakube is a Certified Kubernetes Distribution built on top of CNCF ecosystem that provides an enterprise grade solution following best practices to manage a conformant Kubernetes cluster for on-premise and public cloud providers.
doctr - docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.
oxia - Oxia - Metadata store and coordination system
mmocr - OpenMMLab Text Detection, Recognition and Understanding Toolbox
HealthGPT - Query your Apple Health data with natural language 💬 🩺
kata-containers - Kata Containers is an open source project and community working to build a standard implementation of lightweight Virtual Machines (VMs) that feel and perform like containers, but provide the workload isolation and security advantages of VMs. https://katacontainers.io/
Image2CAD - An application to translate raster image of CAD drawing sheet to a user editable DXF format.
SDV - Synthetic data generation for tabular data
AdelaiDet - AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.