Is there any way to show probability of predicted character from Tesseract 4.0? Thanks.
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I am learning AI for the sake of applying it to the field of chemistry, specifically, molecular generation. I finished learning about how to generate novel molecules using RNN-based architectures such as GRU and LSTM. The process in these architectures is as follows:
the input is a character of a known molecule (represented in a string format), the model's task is to predict the next character, therefore the output is a softmax probability over all characters. Then the loss is computed as the cross-entropy between the predicted character and the real one.
I am now moving to graph neural networks as they seem to provide more advantages over RNN-based architectures. Although I did my research, I could not understand how they work for this task (i.e., molecule generation). I mean, similarly, what is the input, the output, and the loss function that we are trying to minimize in GNN-based molecular generation? Thanks in advance.
I have a dataset of 1000 images of 1 soft toy from different angles. Example: dataset
I have to use some neural networks and train to detect my toy and also output its angle. I want to see: Class Probability Angle.
Example: wanted output
Is there a way to modify SSD or YOLO with Tensorflow or Darknet to modify framework/network to even calculate and output angle in YOLO for example?
While searching the internet I didn't find network example that will do something like that.
I want to train tesseract 4.0.0. But when i searched for it, only training for version 3 is seen. Can someone suggest me some blogs that explains tesseract 4.0.0 training.
For training Neural net based LSTM Tesseract 4.0:
https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00
I have a collection of Chinese character images (with single characters, not text). I need a way to OCR them, i.e. to map them to Unicode.
However, a crucial fact is that many of the images are a bit blurred, or quite small. Thus, the algorithm (or library, or online service) should not just return one Unicode value, but some kind of probability vector which estimates the probability that the given image represents which character.
For example, the image
could have the following distribution:
咳 95%
骇 4%
该 1%
I'd rather not train a neural network myself, since I'm sure that all OCR models are probabilistic underneath. All I'm looking for is an OCR solution that exposes the probabilities for single characters.
I am using caffe to train a deep network, and have set the display at 200 iterations in my solver prototext file. However, instead of getting the loss and accuracy, I am getting a large number of lines of the form
solver.cpp:245] Train net output #{no.}: prob = {no.}.
The prob values are the probabilities of output of softmax layer(the last layer in my network).
This display is of no real use to me. I am interested in seeing only the rate at which the accuracy is evolving with the number of iterations. Can someone suggest a way in which I may print only the relevant parameters to stdout? Is there a way to control in general what is printed by caffe to stdout?
Thanks.
(Note: I am using caffe executable for training on ubuntu)