Færdiggjort

OCR with AI (intelligend data processing)

Job Description:

I already have a python software source-code that converts PDF files into structured Json files.

This is done using OCR and YOLOv5.

The PDF files are supplier invoices. These invoices consist of a header, x items and a footer. There are a subset of predfinded variables (like customer_number, invoice_number, ...) to extract from pdf.

I now need the following adjustments:

1.) the tool needs to be improved. Because it does not recognize the data correctly for the positions.

2.) I need a simple graphical user interface to learn (train) more PDF files. I need a generic solution for this. Today's solution is not generic, but trained and coded for 3 different PDF formats.

3.) the learned models should learn from each other. This means that the tool should learn automatically when new PDF files are trained. If a new format comes along, it should also recognize it as best as possible without necessarily having to train it.

Now I am asking for offers to improve the tool and bring in these 3 points.

These are the requirements of the python tool that must be installed for the tool to run:

# YOLOv5 requirements

# Usage: pip install -r [login to view URL]

# bases ----------------------------------------

flask

easyocr

pdf2image

image slicer

matplotlib>=3.2.2

numpy>=1.18.5

opencv-python>=4.1.1

Pillow>=7.1.2

PyYAML>=5.3.1

requests>=2.23.0

scipy>=1.4.1

torch>=1.7.0 # see [login to view URL] (recommended)

torchvision>=0.8.1

tqdm>=4.64.0

# protobuf<=3.20.1 # [login to view URL]

# logging -------------------------------------

tensorboard>=2.4.1

# wallb

# clearml

# Plot ------------------------------------

pandas>=1.1.4

seaborn>=0.11.0

# Export -------------------------------------

# coremltools>=6.0 # CoreML export

# onnx>=1.9.0 # ONNX export

# onnx-simplifier>=0.4.1 # ONNX simplifier

# nvidia-pyindex # TensorRT export

# nvidia-tensorrt # TensorRT export

# scikit-learn<=1.1.2 # CoreML quantization

# tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos)

# tensorflowjs>=3.9.0 # [login to view URL] export

# openvino-dev # OpenVINO export

# Deploy --------------------------------------

# tritonclient[all]~=2.24.0

# Tools --------------------------------------

ipython # interactive notebook

psutil # system utilization

thop>=0.1.1 # FLOP's computation

# mss # screenshots

# album entries>=1.0.3

# pycocotools>=2.0 # COCO mAP

# roboflow

Færdigheder: Python, Kunstig intelligens, Deep Learning, OCR, Machine Learning (ML)

Om klienten:
( 5 bedømmelser ) Grüt, Switzerland

Projekt ID: #35389087

Tildelt til:

(24 bedømmelser)
5.1

37 freelancere byder i gennemsnit $897 timen for dette job

umg536

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6.6
liveexperts123

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6.6
AITSoft

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6.3
omarpcalimbas

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6.2
arturneskromnyi

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5.9
simonklemp

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5.3
karlmarkus609

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5.3
suyashdhoot

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6.3
gm341473

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5.1
davidstrager

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4.9
anenkovakateryna

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4.8
alamineee

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5.2
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tsmcguinness

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4.0
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3.7
Ashraf714031

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3.8
bengrehl

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3.5
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3.2
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3.1
Namotsuzawa

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3.2