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AI Machine Learning simple project for Mobile devices

The candidate will have to create an APP

that allows object recognition and documents classification

(clustering , similarity, topic extraction, etc.)

from from the device-camera live pictures, from taken pictures,

or from local pictures (or scanned documents like PDF containing scans)

by accessing the local device folders.

They will be tickets, invoices, physical products, labels with lot-numbers, labels with best-before-date, etc.

The APP will mostly be used on mobile devices,

but has to be multiplatform, designed in Python by using QT Framework + Felgo libraries.

The APP has to allow to select and use existing public Machine Learning trained databases,

and extend the selected databases with the data created by processing new pictures

(via device-camera, local files and online files via direct URL).

The APP will then allow to extract data from the identified documents,

and fill-in a custom database with the extracted data by creating a digital version of the scanned document,

with the possibility to export the extracted data in XML format too.

EXAMPLE SCENARIO

Let's imagine an inventory staff man needs to

extract data from a label attached on a product

containing information like a "product lot-number" ad a "best-before-date",

and then digitalize and associate that data to a specific product-name and to

the scanned invoice that lists it.

This is what the staff man should have the possibility to do with the app (in random order):

1) the staff man focuses with the device-camera the invoice paper (listing the items that after

will be focused too), that he will have to mark as arrived to the warehouse:

the focused items that the APP can recognize, will be marked by squares

with their name right below, and the user has to have the possibility

to press 2 buttons on the bottom of the square:

So, an invoice would be marked by a square, with written below:

"Invoice by merchant-name"

The staff man can press one of 2 buttons on the bottom of the square:

OK - "Proceed with this recognition";

ED - "Edit this recognition" ;

when pressing on ED ("Edit this recognition")

the staff man can insert accurate data for the model

to be better trained for future recognition,

marking for example the object as recipe instead of invoice,

or marking as invoice by a merchant instead of another merchant;

when pressing on OK ("Proceed with this recognition")

the APP can classify the kind of document as invoice,

and recognizes the text inside the document,

converting it digitally, and adding its content to the internal

database of the scanned invoices,

associating that particular invoice to the Seller ID

recognized on the invoice itself

(updating the corresponding data if already in the database,

or creating a brand new data if missing).

2) the staff man then focus a physical item in the stock

(a single item, or a pallet, or a package containing multiple items inside,

that will be recognized via the APP with AI thanks to

a Machine Learning model trained on various pictures),

so that the APP recognizes the item(s) focused by the camera,

and simply marks / checks-in the item(s) in the (earlier) digitalized document ;

3) the staff man can then (if needed) make click on the line(s) in the digitalized invoice

containing the information for the recognized item he focused before, so that in a

popup window he can edit the relative quantity of the marked item(s),

if for example to the wharehouse arrive less items than the ones expected.

from the same popup window, the staff man can select a button to take a picture,

so that he can take a picture to the "lot number + best-before-date"

to associate it to the item(s) added / marked as arrived In the digitalized invoice.

---

There are a lot of ready-to-use open source projects doing one or more things requested for this project.:

I can provide collected links to the freelancer / team that will be hired.

기술: Mobile App Development, 인공지능, Machine Learning (ML), Qt, 증강 현실

확대 보기: tensorflow lite android, how to implement machine learning in android studio, tensorflow lite, how to make a machine learning app, tensorflow edge device, machine learning app development, how to deploy machine learning models in android, machine learning on mobile devices, simple project mobile, simple project mobile application, ai machine learning jobs, fast ai machine learning, fast.ai machine learning, windows ai machine learning preview, windows ai machine learning, machine learning projects for mobile applications pdf, deploying machine learning models on mobile, ai machine learning automation, ai machine learning neural networks, blockchain ai machine learning

고용주 소개:
( 0건의 리뷰 ) Naples, Italy

프로젝트 ID: #24475778

이 프로젝트의 입찰 현황은 다음과 같습니다. 입찰자: 9명, 평균 입찰가: €609

shoaybs

Hey, I have checked your project titled " AI Machine Learning simple project for Mobile devices ". I would request you to initiate chat to discuss details but before that firstly check the video proposal I especially 더 보기

€115 EUR (19일 이내)
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fabienbenoit1984

Buongiorno! I'd like to deliver stock inventory app. I'm familiar with theory of probability and computer graphics. I'll do the job blazingly fast. Please, give me a try!

€896 EUR (1일 이내)
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myappsdevelopers

I am expert with 10+ years of experience in creating & designing App & Website Android , Ios Application , shopify/eCommerce websites, PHP, HTML/CSS simple to complex websites. Kindly review some of my design and deve 더 보기

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hsh564cf84accd96

we will do AI Machine Learning simple project for Mobile devices I am writing this proposal in order to work for you in Software and Web Development. We are highly trained professional developers seeking to freelance a 더 보기

€90 EUR (7일 이내)
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Imaad27

Thanks for your job posting! I am machine learning and image processing expert as well as I am very familiar with Python, C++, Android. I have developed a lot image processing projects, such as Face Swap, Face Tracking 더 보기

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rovu9474

My experiences are - Machine Learning o Libraries: kaldi, Tensorflow, Caffe, Keras, Theano, Scikit-learn, Pytorch, Pandas, Numpy o Models: DNN, CNN, RNN+LSTM, TDNN+LSTM, End-to-End Deep Learning - Speech processi 더 보기

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Rose518

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softloft2020

Hi, We have experience in Object Detection and Localization, NLP(Natural Language Processing), Computer Vision(Object Detection, Face Detection), Signals(Text to Speech, Speech to Text), Android app Development We wi 더 보기

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