Captcha_solving

All about creating a dataset, preprocessing images, and creating an actual model to solve captcha

STEP 1 - creating a dataset

2 Méthodes :

unzip the captcha.7z archive, and put all image under the captcha folder
OR use generator.js to create your own captcha(s)

generator.js usage

install generator.js dependencies by using npm install inside the same directory as package.json
change out = './temp' to whatever temporary folder you want. Change SIZE = [720,360] to the size you want (its in width,height) and then change the FORMAT = "webp" to the format you want.
run it : node generator.js , wait until its finished generating captcha.

STEP 2 - pre-processing images for training

install opencv-python using pip, then run process_images by using python process_images.py command.

STEP 3 - creation a model

I based my creation on keras documentation

configuration stuff:

first download requirements : pip install -r requirements.txt
second try to run it and see if it detect any gpu devices (if you have one), if it tells you that you have 0 available gpu and you are on windows, I strongly recommand you to use wsl 2 by following tensorflow tutorial

If you are on other platform and don't see any gpus, use tensorflow tutorial as well, I'm not an expert in this kind of situation

now we are going to talk about actually running the model:

use python MODEL_CREATION.py then wait
check if there is any problem with the sizen it'll tell you img_width: [with], img_height: [height], Press Enter to continue... if its the good size (I made it so it resize the image to be 2 times smaller and does some cropping, so if you have in input a 720, 360 size, you'll see 529x120 size) then press enter.
if it doesn't fit, try creating an issue and ask me why.
if everything is fine, you'll have a popup with image and their labels, if the text doesn't correspond, create an issue.
after some wait you'll have a text Train, Press Enter to continue..., this is the good part, where all the magic happens, after pressing enter it will train your model, all you have to do is wait until its finished.
then it'll automaticly save the model, and show you a panel of image with their corresponding prediction. At this point you're pretty much done

STEP 4 - Re-use the model

put test captcha as unprocessed (as normal, not preprocessed) in a directory named test, then simply run python main.py and voilaa, you should see the prediction and the label of the image
if prediction is bad, try to add more captcha to your dataset

That's all, if you have any problem or question, create an issue!

Captcha_solving

All about creating a dataset, preprocessing images, and creating an actual model to solve captcha

STEP 1 - creating a dataset

2 Méthodes :

unzip the captcha.7z archive, and put all image under the captcha folder
OR use generator.js to create your own captcha(s)

generator.js usage

install generator.js dependencies by using npm install inside the same directory as package.json
change out = './temp' to whatever temporary folder you want. Change SIZE = [720,360] to the size you want (its in width,height) and then change the FORMAT = "webp" to the format you want.
run it : node generator.js , wait until its finished generating captcha.

STEP 2 - pre-processing images for training

install opencv-python using pip, then run process_images by using python process_images.py command.

STEP 3 - creation a model

I based my creation on keras documentation

configuration stuff:

first download requirements : pip install -r requirements.txt
second try to run it and see if it detect any gpu devices (if you have one), if it tells you that you have 0 available gpu and you are on windows, I strongly recommand you to use wsl 2 by following tensorflow tutorial

If you are on other platform and don't see any gpus, use tensorflow tutorial as well, I'm not an expert in this kind of situation

now we are going to talk about actually running the model:

use python MODEL_CREATION.py then wait
check if there is any problem with the sizen it'll tell you img_width: [with], img_height: [height], Press Enter to continue... if its the good size (I made it so it resize the image to be 2 times smaller and does some cropping, so if you have in input a 720, 360 size, you'll see 529x120 size) then press enter.
if it doesn't fit, try creating an issue and ask me why.
if everything is fine, you'll have a popup with image and their labels, if the text doesn't correspond, create an issue.
after some wait you'll have a text Train, Press Enter to continue..., this is the good part, where all the magic happens, after pressing enter it will train your model, all you have to do is wait until its finished.
then it'll automaticly save the model, and show you a panel of image with their corresponding prediction. At this point you're pretty much done

STEP 4 - Re-use the model

put test captcha as unprocessed (as normal, not preprocessed) in a directory named test, then simply run python main.py and voilaa, you should see the prediction and the label of the image
if prediction is bad, try to add more captcha to your dataset

That's all, if you have any problem or question, create an issue!

Captcha_solving

All about creating a dataset, preprocessing images, and creating an actual model to solve captcha

STEP 1 - creating a dataset

2 Méthodes :

unzip the captcha.7z archive, and put all image under the captcha folder
OR use generator.js to create your own captcha(s)

generator.js usage

install generator.js dependencies by using npm install inside the same directory as package.json
change out = './temp' to whatever temporary folder you want. Change SIZE = [720,360] to the size you want (its in width,height) and then change the FORMAT = "webp" to the format you want.
run it : node generator.js , wait until its finished generating captcha.

STEP 2 - pre-processing images for training

install opencv-python using pip, then run process_images by using python process_images.py command.

STEP 3 - creation a model

I based my creation on keras documentation

configuration stuff:

first download requirements : pip install -r requirements.txt
second try to run it and see if it detect any gpu devices (if you have one), if it tells you that you have 0 available gpu and you are on windows, I strongly recommand you to use wsl 2 by following tensorflow tutorial

If you are on other platform and don't see any gpus, use tensorflow tutorial as well, I'm not an expert in this kind of situation

now we are going to talk about actually running the model:

use python MODEL_CREATION.py then wait
check if there is any problem with the sizen it'll tell you img_width: [with], img_height: [height], Press Enter to continue... if its the good size (I made it so it resize the image to be 2 times smaller and does some cropping, so if you have in input a 720, 360 size, you'll see 529x120 size) then press enter.
if it doesn't fit, try creating an issue and ask me why.
if everything is fine, you'll have a popup with image and their labels, if the text doesn't correspond, create an issue.
after some wait you'll have a text Train, Press Enter to continue..., this is the good part, where all the magic happens, after pressing enter it will train your model, all you have to do is wait until its finished.
then it'll automaticly save the model, and show you a panel of image with their corresponding prediction. At this point you're pretty much done

STEP 4 - Re-use the model

put test captcha as unprocessed (as normal, not preprocessed) in a directory named test, then simply run python main.py and voilaa, you should see the prediction and the label of the image
if prediction is bad, try to add more captcha to your dataset

That's all, if you have any problem or question, create an issue!

Captcha_solving

All about creating a dataset, preprocessing images, and creating an actual model to solve captcha

STEP 1 - creating a dataset

2 Méthodes :

unzip the captcha.7z archive, and put all image under the captcha folder
OR use generator.js to create your own captcha(s)

generator.js usage

install generator.js dependencies by using npm install inside the same directory as package.json
change out = './temp' to whatever temporary folder you want. Change SIZE = [720,360] to the size you want (its in width,height) and then change the FORMAT = "webp" to the format you want.
run it : node generator.js , wait until its finished generating captcha.

STEP 2 - pre-processing images for training

install opencv-python using pip, then run process_images by using python process_images.py command.

STEP 3 - creation a model

I based my creation on keras documentation

configuration stuff:

first download requirements : pip install -r requirements.txt
second try to run it and see if it detect any gpu devices (if you have one), if it tells you that you have 0 available gpu and you are on windows, I strongly recommand you to use wsl 2 by following tensorflow tutorial

If you are on other platform and don't see any gpus, use tensorflow tutorial as well, I'm not an expert in this kind of situation

now we are going to talk about actually running the model:

use python MODEL_CREATION.py then wait
check if there is any problem with the sizen it'll tell you img_width: [with], img_height: [height], Press Enter to continue... if its the good size (I made it so it resize the image to be 2 times smaller and does some cropping, so if you have in input a 720, 360 size, you'll see 529x120 size) then press enter.
if it doesn't fit, try creating an issue and ask me why.
if everything is fine, you'll have a popup with image and their labels, if the text doesn't correspond, create an issue.
after some wait you'll have a text Train, Press Enter to continue..., this is the good part, where all the magic happens, after pressing enter it will train your model, all you have to do is wait until its finished.
then it'll automaticly save the model, and show you a panel of image with their corresponding prediction. At this point you're pretty much done

STEP 4 - Re-use the model

put test captcha as unprocessed (as normal, not preprocessed) in a directory named test, then simply run python main.py and voilaa, you should see the prediction and the label of the image
if prediction is bad, try to add more captcha to your dataset

That's all, if you have any problem or question, create an issue!

Captcha_solving

All about creating a dataset, preprocessing images, and creating an actual model to solve captcha

STEP 1 - creating a dataset

2 Méthodes :

unzip the captcha.7z archive, and put all image under the captcha folder
OR use generator.js to create your own captcha(s)

generator.js usage

install generator.js dependencies by using npm install inside the same directory as package.json
change out = './temp' to whatever temporary folder you want. Change SIZE = [720,360] to the size you want (its in width,height) and then change the FORMAT = "webp" to the format you want.
run it : node generator.js , wait until its finished generating captcha.

STEP 2 - pre-processing images for training

install opencv-python using pip, then run process_images by using python process_images.py command.

STEP 3 - creation a model

I based my creation on keras documentation

configuration stuff:

first download requirements : pip install -r requirements.txt
second try to run it and see if it detect any gpu devices (if you have one), if it tells you that you have 0 available gpu and you are on windows, I strongly recommand you to use wsl 2 by following tensorflow tutorial

If you are on other platform and don't see any gpus, use tensorflow tutorial as well, I'm not an expert in this kind of situation

now we are going to talk about actually running the model:

use python MODEL_CREATION.py then wait
check if there is any problem with the sizen it'll tell you img_width: [with], img_height: [height], Press Enter to continue... if its the good size (I made it so it resize the image to be 2 times smaller and does some cropping, so if you have in input a 720, 360 size, you'll see 529x120 size) then press enter.
if it doesn't fit, try creating an issue and ask me why.
if everything is fine, you'll have a popup with image and their labels, if the text doesn't correspond, create an issue.
after some wait you'll have a text Train, Press Enter to continue..., this is the good part, where all the magic happens, after pressing enter it will train your model, all you have to do is wait until its finished.
then it'll automaticly save the model, and show you a panel of image with their corresponding prediction. At this point you're pretty much done

STEP 4 - Re-use the model

put test captcha as unprocessed (as normal, not preprocessed) in a directory named test, then simply run python main.py and voilaa, you should see the prediction and the label of the image
if prediction is bad, try to add more captcha to your dataset

That's all, if you have any problem or question, create an issue!

Captcha_solving

All about creating a dataset, preprocessing images, and creating an actual model to solve captcha

STEP 1 - creating a dataset

2 Méthodes :

unzip the captcha.7z archive, and put all image under the captcha folder
OR use generator.js to create your own captcha(s)

generator.js usage

install generator.js dependencies by using npm install inside the same directory as package.json
change out = './temp' to whatever temporary folder you want. Change SIZE = [720,360] to the size you want (its in width,height) and then change the FORMAT = "webp" to the format you want.
run it : node generator.js , wait until its finished generating captcha.

STEP 2 - pre-processing images for training

install opencv-python using pip, then run process_images by using python process_images.py command.

STEP 3 - creation a model

I based my creation on keras documentation

configuration stuff:

first download requirements : pip install -r requirements.txt
second try to run it and see if it detect any gpu devices (if you have one), if it tells you that you have 0 available gpu and you are on windows, I strongly recommand you to use wsl 2 by following tensorflow tutorial

If you are on other platform and don't see any gpus, use tensorflow tutorial as well, I'm not an expert in this kind of situation

now we are going to talk about actually running the model:

use python MODEL_CREATION.py then wait
check if there is any problem with the sizen it'll tell you img_width: [with], img_height: [height], Press Enter to continue... if its the good size (I made it so it resize the image to be 2 times smaller and does some cropping, so if you have in input a 720, 360 size, you'll see 529x120 size) then press enter.
if it doesn't fit, try creating an issue and ask me why.
if everything is fine, you'll have a popup with image and their labels, if the text doesn't correspond, create an issue.
after some wait you'll have a text Train, Press Enter to continue..., this is the good part, where all the magic happens, after pressing enter it will train your model, all you have to do is wait until its finished.
then it'll automaticly save the model, and show you a panel of image with their corresponding prediction. At this point you're pretty much done

STEP 4 - Re-use the model

put test captcha as unprocessed (as normal, not preprocessed) in a directory named test, then simply run python main.py and voilaa, you should see the prediction and the label of the image
if prediction is bad, try to add more captcha to your dataset

That's all, if you have any problem or question, create an issue!

Captcha_solving

All about creating a dataset, preprocessing images, and creating an actual model to solve captcha

STEP 1 - creating a dataset

2 Méthodes :

unzip the captcha.7z archive, and put all image under the captcha folder
OR use generator.js to create your own captcha(s)

generator.js usage

install generator.js dependencies by using npm install inside the same directory as package.json
change out = './temp' to whatever temporary folder you want. Change SIZE = [720,360] to the size you want (its in width,height) and then change the FORMAT = "webp" to the format you want.
run it : node generator.js , wait until its finished generating captcha.

STEP 2 - pre-processing images for training

install opencv-python using pip, then run process_images by using python process_images.py command.

STEP 3 - creation a model

I based my creation on keras documentation

configuration stuff:

first download requirements : pip install -r requirements.txt
second try to run it and see if it detect any gpu devices (if you have one), if it tells you that you have 0 available gpu and you are on windows, I strongly recommand you to use wsl 2 by following tensorflow tutorial

If you are on other platform and don't see any gpus, use tensorflow tutorial as well, I'm not an expert in this kind of situation

now we are going to talk about actually running the model:

use python MODEL_CREATION.py then wait
check if there is any problem with the sizen it'll tell you img_width: [with], img_height: [height], Press Enter to continue... if its the good size (I made it so it resize the image to be 2 times smaller and does some cropping, so if you have in input a 720, 360 size, you'll see 529x120 size) then press enter.
if it doesn't fit, try creating an issue and ask me why.
if everything is fine, you'll have a popup with image and their labels, if the text doesn't correspond, create an issue.
after some wait you'll have a text Train, Press Enter to continue..., this is the good part, where all the magic happens, after pressing enter it will train your model, all you have to do is wait until its finished.
then it'll automaticly save the model, and show you a panel of image with their corresponding prediction. At this point you're pretty much done

STEP 4 - Re-use the model

put test captcha as unprocessed (as normal, not preprocessed) in a directory named test, then simply run python main.py and voilaa, you should see the prediction and the label of the image
if prediction is bad, try to add more captcha to your dataset

That's all, if you have any problem or question, create an issue!

Captcha_solving

All about creating a dataset, preprocessing images, and creating an actual model to solve captcha

STEP 1 - creating a dataset

2 Méthodes :

unzip the captcha.7z archive, and put all image under the captcha folder
OR use generator.js to create your own captcha(s)

generator.js usage

install generator.js dependencies by using npm install inside the same directory as package.json
change out = './temp' to whatever temporary folder you want. Change SIZE = [720,360] to the size you want (its in width,height) and then change the FORMAT = "webp" to the format you want.
run it : node generator.js , wait until its finished generating captcha.

STEP 2 - pre-processing images for training

install opencv-python using pip, then run process_images by using python process_images.py command.

STEP 3 - creation a model

I based my creation on keras documentation

configuration stuff:

first download requirements : pip install -r requirements.txt
second try to run it and see if it detect any gpu devices (if you have one), if it tells you that you have 0 available gpu and you are on windows, I strongly recommand you to use wsl 2 by following tensorflow tutorial

If you are on other platform and don't see any gpus, use tensorflow tutorial as well, I'm not an expert in this kind of situation

now we are going to talk about actually running the model:

use python MODEL_CREATION.py then wait
check if there is any problem with the sizen it'll tell you img_width: [with], img_height: [height], Press Enter to continue... if its the good size (I made it so it resize the image to be 2 times smaller and does some cropping, so if you have in input a 720, 360 size, you'll see 529x120 size) then press enter.
if it doesn't fit, try creating an issue and ask me why.
if everything is fine, you'll have a popup with image and their labels, if the text doesn't correspond, create an issue.
after some wait you'll have a text Train, Press Enter to continue..., this is the good part, where all the magic happens, after pressing enter it will train your model, all you have to do is wait until its finished.
then it'll automaticly save the model, and show you a panel of image with their corresponding prediction. At this point you're pretty much done

STEP 4 - Re-use the model

put test captcha as unprocessed (as normal, not preprocessed) in a directory named test, then simply run python main.py and voilaa, you should see the prediction and the label of the image
if prediction is bad, try to add more captcha to your dataset

That's all, if you have any problem or question, create an issue!

Name		Name	Last commit message	Last commit date
captcha		captcha
temp		temp
LICENSE		LICENSE
MODEL_CREATION.py		MODEL_CREATION.py
README.md		README.md
generator.js		generator.js
main.py		main.py
package.json		package.json
pre_processing.py		pre_processing.py
prediction.py		prediction.py
process_images.py		process_images.py
requirements.txt		requirements.txt

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Captcha_solving

STEP 1 - creating a dataset

generator.js usage

STEP 2 - pre-processing images for training

STEP 3 - creation a model

configuration stuff:

STEP 4 - Re-use the model

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Captcha_solving

STEP 1 - creating a dataset

generator.js usage

STEP 2 - pre-processing images for training

STEP 3 - creation a model

configuration stuff:

STEP 4 - Re-use the model

About

Topics

Resources

Stars

Watchers

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Used by

Contributors

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Latest commit

History

Folders and files

Repository files navigation

Captcha_solving

STEP 1 - creating a dataset

generator.js usage

STEP 2 - pre-processing images for training

STEP 3 - creation a model

configuration stuff:

STEP 4 - Re-use the model

About

Topics

Resources

Stars

Watchers

Forks

Used by

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

Captcha_solving

STEP 1 - creating a dataset

generator.js usage

STEP 2 - pre-processing images for training

STEP 3 - creation a model

configuration stuff:

STEP 4 - Re-use the model

About

Topics

Resources

Stars

Watchers

Forks

Used by

Contributors

Languages