omni-us / research-ganwriting Goto Github PK

I was wondering if you could share the actual code for Fig. 7 since the reported code tt.word_ladder.py is related to Fig. 8 of the manuscript. Thanks in advance

Hi, sir, this is a very cool and awesome article, and thank you for sharing the code.

I noticed that is displayed in your code,
"# NUM_CHANNEL = 15
NUM_CHANNEL = 50"
but the paper mentioned "K = 15 word images from the same writer w_i".

Did I miss something, or what does "NUM_CHANNEL" mean?
Thank you very much.
:D

When looking at the image of the architecture overview, I noticed two things that were reflected differently in the code.

1. The noise that is added to is not present in the code. Am I missing something here?
2. The cubes that represent the shape of are misleading because they imply that when merging and the channel dimension changes. However, the linear layer here halves the number of channels of the combined feature maps. This way the number of channels is .

If I am not mistaken or have missed something, would it be possible to fix those issues?
Because besides those minor flaws, the graphic is really beautiful and provides a great overview of the network's architecure.

I have read your article GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images and your code, and I am confused about how to calculate the FID of the OOV words.
I noticed that in your article you mentioned that you prepared 400 unique words to test the OOV performance, but there are only 80 words in your file corpora_english/oov.common_words , I have a few questions:

1. If it is actually 400 words, could you please give me the word list?
2. Is the OOV FID calculated by averaging of the FID scores of each handwriting style or directly performing evaluation on randomly sampled generated images?

Thanks for releasing the code of your awesome paper. I have a question about the loss calculation in network_pro.py. You generate a new text using the new_ed1 function in load_data.py and use the generated image of the new text string to calculate the discriminator loss, writer classifier loss and the recognition loss together with the generated image of the target text string. What is the purpose of this processing?

Hi @leitro @hendraet, thanks for sharing your amazing work.

Could you share your pre-trained model so that we can get rid of retraining one and play it right away?

I will be grateful if you could provide your pre-trained model. Thanks in advance.

Hi, I was impressed by your great work. I realized there is a pretrained model which can generation words within length 7. But I am wondering can I use this repo to generate a line of words? Like I set the max character as a super large number, then retrain a new model, can that new one generate a line?

Hi. I don't have a deep understanding of all this, so please bear with me.

I am working hobby project with the ornate handwriting of a medieval manuscript. The manuscript is in Latin. There are no letter "j"s (i is used), no "k"s (didn't exist), no "v"s (u is used), no "w"s (didn't exist), and very few "y"s.

Would research-GANwriting be capable of doing either of the following tasks?

Produce the letters that don't exist based on the letters that do.

Produce multiple, unique instances of letters that are few in number. (The letters that exist in abundance slightly vary from one to the next, because this is handwriting. So an "e", for example, looks slightly different every time. I'm asking if MC-GAN could create more "y"s, for example, with each one slightly varying from the others, yet plausibly the product of the original scribe.)

Thank you!

Thanks very much for sharing your work with us.

I wish to ask for your help. I have trained the model from scratch for more than 4000 epochs, but, it is not learning. I did not change anything in your code except the directory for IAM. The pre-trained model that you shared was trained for 2000 epochs and it is a thousand times better than the model I trained for 4000 epochs.

Please, let me know if I have to make any changes in your code for learning to take place.

Thanks