Comments (2)
To be clear, this software only does cropped text recognition. That is, it doesn't take an arbitrary image and identify the bounding boxes of the text (or words).
However, assuming you have something like the example above, this model could in theory be used to identify the separate words if you train with a literal space among the output character labels, and you generate training data with multiple words (containing the space character label). This keras example is an example that does so (with two words).
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I am using CTPN to prepare the cropped images from the bounding boxes it provides. It doesn't make bounding boxes on single words but on multiple words. Thanks for confirming that my training data would need a literal space. I will try playing around to see if there are other ways. Thanks
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Related Issues (20)
- Training error HOT 9
- How to deal with single character input HOT 2
- Using Multiple GPU as a train_device HOT 3
- ctc_loss_calculator.cc Not a valid path HOT 3
- are there any pretrain model file HOT 3
- Input shapes: [72,357,1], [4] and with input tensors computed HOT 3
- It's possible to use a pre-trained model? HOT 1
- CuDnn 7.2.1 HOT 1
- get not good result, HOT 2
- FineTune ! HOT 3
- Model learns nothing about certain characters. HOT 1
- TypeError HOT 4
- Fixed sequence length HOT 1
- Training Error when using my data HOT 1
- 0% GPU-Util when testing HOT 1
- Train on vertical patches HOT 1
- Question Regarding End Model HOT 3
- confidence on sess passing HOT 6
- How to convert .ckpt model to SavedModel .pb format for hosting with Tensorflow Model Serving? HOT 6
- How to batch inference? HOT 5
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