I am currently using pytesseract image_to_data to compute the average confidence level of the strings. However, I am interested to know the theory behind it. What kind of algorithm is involved in such computation? Could anyone enlighten me?
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I recently came upon a question that I haven't seen anywhere else while searching about lossy compression. Can you determine the quality lost through a certain algorithm? I have been asking around and it seems like that there isn't a sure way to determine the quality lost compared to an original image and can only be differentiated by the naked eye. Is there an algorithm that shows % lost or blended?
I would really appreciate it if someone could give me some insight into this matter.
You can use lots of metrics to measure quality loss. But, of course, each metric will interpret quality loss differently.
One direction, following the suggestion already commented, would be to use something like the Euclidian distance or the mean squared error between the original and the compressed image (considered as vectors). There are many more metrics of this "absolute" kind.
The above will indicate a certain quality loss but the result may not correlate with human perception of quality. To give more weight to perception you can inspect the structural similarity of the images and use the structural similarity index measure (SSIM) or one of its variants. Another algorithm in this area is butteraugli.
In Python, for instance, there is an implementation of SSIM in the scikit-image package, see this example.
The mentioned metrics have in common that they do not return a percentage. If this is crucial to you, another conversion step will be necessary.
I am trying to fit a custom distribution to a large (~O(500,000) measurements) dataset using scipy. I have derived a theoretical PDF based on some other factors, but both by hand and using symbolic integration software I cannot find an exact form of the CDF.
Currently, simply evaluating 1000 random samples from my custom distribution is expensive, which I believe is due to the need to invert an unknown CDF. If I cannot find an explicit form of the CDF and it's inverse, is there anything else I can do to speed up usage of this distribution?
I've used maple, matlab and Sympy to try and determine a CDF, yet none give a result. I also tried down-sampling my data whilst still retaining the tail attributes, but this still required so much data that doing anything with the distribution was slow.
My distribution is a sub-class of SciPy's rv_continuous class.
Thanks for any advice.
This sounds like you want to sample from a Kernel Density Estimation of the probability distribution. While Scipy does offer a Gaussian Kernel package, for that many measurements you would be much better off using sklearn's implementation. A good resource with code examples can be found on Jake VanderPlas's blog.
I'm implementing an character recognition system with Hidden Markov Model(HMM). I have used skeleton to extract features of image. And I thought to use HMM for training images.
My question is how I can give those features to HMM? I got to know that I have to save those features into a file and then that file should feed to the HMM.
Can someone please help me? I am stuck here for two months. Still, I couldn't find the solution for this.
Appreciate your help a lot.
I was passing by and just saw this question.
Maybe you looked somewhere because your question is almost a month ago.
You give the features to HMM by clustering your data, you can use k-means, or you can use windows with lengths. If you use k-means, you will obtain the centers, you can use the centers to obtain the features, after this you need to crossfold validation to see if it really learns the features you labeled. Also K-means gives you the states and the initial transition probabilities.
Hope this helps you
I have been trying to develop an OCR engine by myself. After researching the topic a bit I have come to the conclusion that there are 4 major steps involved :
Pre-processing the image [de-skewing, image contrast, binarize, etc.]
Segment the image into the characters [to make it easier to process each character individually]
Identify the chracter through feature extraction / comparison and classification.
Post-processing the image [to increase the chances of getting an optimal solution.]
I am hopelessly lost after the 1st step! Can somebody please help me out by telling how to perform character segregation & feature extraction ? I'll be extremely grateful even if you can provide me a link which points me in the right direction.
Thanks in advance! :)
There is a paper called self-tuning spectral clustering by Zelnik-Manor and Perona. Here is the link to their page for paper and code written in Matlab:
Self-Tuning Spectral Clustering
This method can perform image segmentation. Another thing you may want to look into is topic-modeling on images for feature extraction. Anything by Blei will also be useful.
The Computer Vision System Toolbox now has the ocr function that can save you the trouble.
I am new to neural networks and, to get grip on the matter, I have implemented a basic feed-forward MLP which I currently train through back-propagation. I am aware that there are more sophisticated and better ways to do that, but in Introduction to Machine Learning they suggest that with one or two tricks, basic gradient descent can be effective for learning from real world data. One of the tricks is adaptive learning rate.
The idea is to increase the learning rate by a constant value a when the error gets smaller, and decrease it by a fraction b of the learning rate when the error gets larger. So basically the learning rate change is determined by:
+(a)
if we're learning in the right direction, and
-(b * <learning rate>)
if we're ruining our learning. However, on the above book there's no advice on how to set these parameters. I wouldn't expect a precise suggestion since parameter tuning is a whole topic on its own, but just a hint at least on their order of magnitude. Any ideas?
Thank you,
Tunnuz
I haven't looked at neural networks for the longest time (10 years+) but after I saw your question I thought I would have a quick scout about. I kept seeing the same figures all over the internet in relation to increase(a) and decrease(b) factor (1.2 & 0.5 respectively).
I have managed to track these values down to Martin Riedmiller and Heinrich Braun's RPROP algorithm (1992). Riedmiller and Braun are quite specific about sensible parameters to choose.
See: RPROP: A Fast Adaptive Learning Algorithm
I hope this helps.