Comments (2)
Yes. RNN has good performance on OCR task, such as hand writing recognition. Generally, you need to segment handwritten word images at first, and then recognize each word in the image.
The feature selection is the key important part. You can design and combine your feature manually, and RNN can also help you to generate features automatically, such as embedding input pixels into vectors.
With reasonable feature set, you can choose a classifier to detect which word is in the image.
from rnnsharp.
I'm a newbie on Machine Learning but very interested in it.
do you mean the segmentation is needed before ocr using RNN ?
I'm not good at c#, is there some document for RnnSharp ?
would you like to make a little ocr task demo using RNNSHARP when you are free?
Thanks !
Best regards,
from rnnsharp.
Related Issues (20)
- Impact of run-time feature HOT 4
- LSTM RNN HOT 2
- Plan to Use LSTM for Time Series HOT 1
- Need Help With PRETRAIN_TYPE=Autoencoder HOT 2
- Null reference exception with ForwardRNN HOT 3
- Incremental training HOT 3
- imncremental training NullReferenceException after last update HOT 2
- Training takes too long!! HOT 20
- Using the library from .Net Core HOT 3
- maybe it's time to support CUDA ! HOT 2
- AdvUtils.dll Issue HOT 3
- puzzled about function : logsumexp HOT 1
- A question regarding usage or RNNSharp for a particular problem
- NullReferenceException in DropoutLayer
- System Total Fail due to double-Number conversion problems for non "es-US" Cultures
- A few questions regarding how to exploit RNNSharp
- Is there a way to further reduce file size of the model without affecting network size?
- Time Complexity
- RNNSharp Pseudo code
- About inference time
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