Hi there,

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I have a task where I need to extract specific elements (name, surname, company name) from text present in images which are forms and other administrative documents (example here), but there are many types of documents and each are structured differently.

Sometimes the required elements will be handwritten, sometimes not. They also will be placed differently on the page depending on the document.

And the data is not annotated. (do I need to annotate it myself before, or can it be made unsupervised ?)

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So I’m at a real loss here on what to do. ^{(I’m a beginner in the field so please bear with me :}.))

I’m really not an expert on NLP nor text extraction or detection. Mostly worked on images with classical datasets, but with neural achitectures mostly.

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Classic OCR techniques I tested like Tesseract don’t work at all for handwritten text (maybe there’s appropriate configuration for this ?).

Since the data is so unstructured (to me) I thought of using neural networks and especially a Faster RCNN model which I would train to detect only the three elements I mentioned before (after having annotated the data myself) as if they were specific objects like for classical Faster RCNN models. Paper ^{(use hub of science}) on researchers successfully doing it.

But someone I talked to told me it was easy to do even with the constraints I told you, and that neural networks weren’t even needed, nor was annotating.

My guess at the beginning was that I needed to find a way to extract the whole text from the image (with the handwritten text, and well positioned in relation to the printed text) and then find a way to give my algorithm insight on how to distinguish names from the rest.

How could I do this with simple machine learning ? Is there a dataset of names and surnames that I can train my model on so that it would understand their intrinsic characteristics be able to distinguish them from other text ?

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Thanks a lot in advance for your help !

I attended this year’s Eastern European Machine Learning Summer School and wanted to share my experience.

I tried to summarize all the lectures and events in this blog post:

https://medium.com/@richardcsaky/eeml-2019-a-subjective-retelling-67448909e64b

I think there will be a 2020 edition, and I definitely recommend applying to it!

Hi! I built http://ganarts.ru – StyleGAN app for generating prints for T-shirts in the style of contemporary art. Feel free to print these masterpieces on your T-shirts!

StyleGAN trained on 40,000 images, the training process takes 3 days on 4x1080ti GPU. Every 30 seconds there are 9 new images, and the old ones are deleted.

I have been recently exploring how to make GANs work with HD images, without the need of expensive GPUs and long computation times.

This is my result:

https://towardsdatascience.com/style-transfer-with-gans-on-hd-images-88e8efcf3716

It surely isn’t anything new, but I have never heard of this super easy solution being applied to this kind of goal.

I would love to know if there are other methods in literature similar to this one.

Announcement

We’re thrilled to share a comprehensive, large-scale dataset featuring the raw sensor camera and LiDAR inputs as perceived by a fleet of multiple, high-end, autonomous vehicles in a bounded geographic area. This dataset also includes high quality, human-labelled 3D bounding boxes of traffic agents, an underlying HD spatial semantic map.

With this, we aim to empower the community, stimulate further development, and share our insights into future opportunities from the perspective of an advanced industrial Autonomous Vehicles program.

Link to the dataset’s page with more technical information and examples.

There’s also going to be a competition at NeurIPS this year, which I assume to be the recently added “AI Driving Olympics” at the very bottom of the competitions page…

I’ve been working on a small project for a few years, and I finally made a simple web interface. Sobert is a chat bot using a 4-layer, 113 million parameter byte-level Grid GRU language model trained on ~370MB of chat data collected over the years.

Go to https://sobert.hammy.sk/, click connect and say hello! It’s one shared chat, so don’t post anything private – everyone in the chat can see it.

Be careful, Sobert understands many topics, and not all of them are SFW. The web version is also new and not well tested, and won’t be able to handle too many users, but the Telegram version should be quite reliable.

Code for all the related parts

Hello, I’m Hendra. I’m an undergraduate students who is working on final project so I can graduate. My lecturer asked me to use ANN for energy disaggregation system. I’m using keras machine learning with tensor flow as the backend. Lately I have a problem on making a correct model for my case and got bad accuracy score. I have 4 lamps with various power consumption and I want to predict the power consumption of each lamp using ANN. My dataset is on link. On the dataset, 1 means ON and 0 means OFF. I’m using jupyter notebook as its interface. Can anyone give me some idea about what models I can use for this type of problem? I’m currently stumped. Any kind of help is really appreciated. Thank you….. Here is the dataset : https://drive.google.com/file/d/1ZrW083twfEIHZzqGuaDYA7jjO2s5P44p/view?usp=sharing

Hi, pretty new to computer vision here. I’ve seen a lot of standardized datasets for CV such as MNIST, CIFAR-10, ImageNet, etc. ImageNet in particular stood out to me because of how specific a lot of the image classifications were, such as “pug” or “dalmation”. However, is there a model out there which focuses on classifying attributes of the object e.g. dog color, or a standardized dataset that would enable that analysis?

Pointing me towards the relevant literature would be really appreciated! I’ve done my Googling but I think I might not know the specific terms used in this domain to describe stuff like this, because I keep running into generic object classification and detection without the object attributes.