I have a few questions regarding the fine-tuning process.
I'm building an app that is able to recognize data from the following documents:
ID Card
Driving license
Passport
Receipts
All of them have different fonts (especially receipts) and it is hard to match exactly the same font and I will have to train the model on a lot of similar fonts.
So my questions are:
Should I train a separate model for each of the document types for better performance and accuracy or it is fine to train a single eng model on a bunch of fonts that are similar to the fonts that are being used on this type of documents?
How many pages of training data should I generate per font? By default, I think tesstrain.sh generates around 4k pages.
Maybe any suggestions on how I can generate training data that is closest to real input data
How many iterations should be used?
For example, if I'm using some font that has a high error rate and I want to target 98% - 99% accuracy rate.
As well maybe some of you had experience working with this type of documents and maybe you know some common fonts that are being used for these documents?
I know that MRZ in passport and id cards is using OCR-B font, but what about the rest of the document?
Thanks in advance!
Ans 1
you can train a single model to achieve the same but if you want to detect different languages then I think you will need different models.
Ans 2
If you are looking for some datasets then have a look at this Mnist Png Dataset which has digits as well as alphabets from various computer-based fonts. Here is a link to some starter code to use the data set implemented in Pytorch.
Ans 3
You can use optuna to find the best set of params for your model, but you will need some of the
using-optuna-to-optimize-pytorch-hyperparameters
Have a look at these
PAN-Card-OCR
document-details-parsing-using-ocr
They are trying to achieve similar task.
Hope it answers your Question...!
I would train a classifier on the 4 different types to classify an ID, license, passport, receipts. Basically so you know that a passport is a passport vs a drivers license ect. Then I would have 4 more models that are used for translating each specific type (passport, drivers license, ID, and receipts). It should be noted that if you are working with multiple languages this will likely mean making 4 models based each specific language meaning that if you have L languages you make need 4*L number of models for translating those.
Likely a lot. I don’t think that font is really an issue. Maybe what you should do is try and define some templates for things like drivers license and then generate based on that template?
This is the least of your problems, just test for this.
Assuming you are referring to a ML data model that might be used to perform ocr using computer vision I'd recommend to:
Setup your taxonomy as required by your application requirements.
This means to categorize the expected font sets per type of scanned document (png,jpg tiff etc.) to include inside the appropriate dataset. Select the fonts closest to the ones being used as well as the type of information you need to gather (Digits only, Alphabetic characters).
Perform data cleanup on your dataset and make sure you have homogenous data for the OCR functionality. For example, all document images should be of png type, with max dimensions of 46x46 to have an appropriate training model. Note that higher resolution images and smaller scale means higher accuracy.
Cater for handwritting as well, if you have damaged or non-visible font images. This might improve character conversion options in cases that fonts on paper are not clearly visible/worn out.
In case you are using keras module with TF on mnist provided datasets, setup a cancellation rule for ML model training when you reach 98%-99% accuracy for more control in case you expect your fonts in images to be error-prone (as stated above). This helps avoid higher margin of errors when you have bad images in your training dataset. For a dataset of 1000+ images, a good setting would be using TF Dense of 256 and 5 epochs.
A sample training dataset can be found here.
If you just need to do some automation with your application or do data entry that requires OCR conversion from images, a good open source solution would be to use information gathering automatically via PSImaging module (Powershell) use the degrees of confidence retrieved (from png) and run them against your current datasets to improve your character match accuracy.
You can find the relevant link here
Related
I've used three parallel sentence files to train my custom translator model. No dictionary files and no tuning files too. After training is finished and I've checked test results, I want to make some adjustments in the model. And here are several questions:
Is it possible to tune the model after training? Am I right that the model can't be changed and the only way is to train a new model?
The best approach to adjusting the model is to use tune files. Is it correct?
There is no way to see an autogenerated tune file, so I have to provide my own tuning file for a more manageable tuning process. Is it so?
Could you please describe how the tuning file is generated, when I have 3 sentence files with different amount of sentences, which is: 55k, 24k and 58k lines. Are all tuning sentences is from the first file or from all three files proportionally to their size? Which logic is used?
I wish there were more authoritative answers on this, I'll share what I know as a fellow user.
What Microsoft Custom Translator calls "tuning data" is what is normally known as a validation set. It's just a way to avoid overfitting.
Is it possible to tune the model after training? Am I right that the model can't be changed and the only way is to train a new model?
Yes, with Microsoft Custom Translator you can only train a model based on the generic category you have selected for the project.
(With Google AutoML technically you can choose to train a new model based on one of your previous custom models. However, it's also not usable without some trial and error.)
The best approach to adjusting the model is to use tune files. Is it correct?
It's hard to make a definitive statement on this. The training set also has an effect. A good validation set on top of a bad training set won't get us good results.
There is no way to see an autogenerated tune file, so I have to provide my own tuning file for a more manageable tuning process. Is it so?
Yes, it seems to me that if you let it decide how to split the training set into the training set, tuning set and test set, you can only download the training set and the test set.
Maybe neither includes the tuning set, so theoretically you can diff them. But that doesn't solve the problem of the split being different between different models.
... Which logic is used?
Good question.
I have been trying to tackle a problem where I need to track multiple people through multiple camera viewpoints on a real-time basis.
I found a solution DeepCC (https://github.com/daiwc/DeepCC) on DukeMTMC dataset but unfortunately, this solution has been taken down because of data confidentiality issues. They were using Fast R-CNN for object detection, triplet loss for Re-identification and DeepSort for real-time multiple object tracking.
Questions:
1. Can someone share some other resources regarding the same problem?
2. Is there a way to download and still use the DukeMTMC database for multiple tracking problem?
3. Is anyone aware when the official website (http://vision.cs.duke.edu/DukeMTMC/) will be available again?
Please feel free to provide different variations of the question :)
Intel OpenVINO framewors has all part of this task:
Objects detection with pretrained Faster RCNN, SSD or YOLO.
Reidentification models.
And complete demo application.
And you can use another models. Or if you want to use detection on GPU then take opencv_dnn_cuda for detection and OpenVINO for reidentification.
A good deep learning library that I have used in the past for my work is called Mask R-CNN, or Mask Regions-Convolutional Neural-Network. Although I have only used this algorithm on images and not on videos, the same principles apply, and it's very easy to make the transition to detection objects in a video. The algorithm uses Tensorflow and Keras, where you can split your input data, i.e images of people, into two sets, training, and validation.
For training, use a third party software like via, to annotate the people in the images. After the annotations have been drawn, you will export a JSON file with all annotations drawn, which will be used for the training process. Do the same thing for the validation phase, BUT make sure the images in the validation have not been seen before by the algorithm.
Once you have annotated both groups and generated JSON files, you then can start training the algorithm. Mask R-CNN makes it very easy to train, with all you need to do is pass one line full of commands to start it. If you want to train data on your GPU instead of your CPU, then install Nvidia's CUDA, which works very well with supported GPUs, and requires no coding after the installation.
During the training stage, you will be generating weights files, which are stored in the .h5 format. Depending on the number of epochs you choose, there will be a weights file generated per epoch. Once the training has finished, you then will just have to reference that weights file anytime you want to detect relevant objects, i.e. in your video feed.
Some important info:
Mask R-CNN is somewhat of an older algorithm, but it still works flawlessly today. Although some people have updated the algorithm to Tenserflow 2.0+, to get the best use out of it, use the following.
Tensorflow-gpu 1.13.2+
Keras 2.0.0+
CUDA 9.0 to 10.0
Honestly, the hardest part for me in the past was not using the algorithm, but finding the right versions of Tensorflow, Keras, and CUDA, that all play well with each other, and don't error out. Although the above-mentioned versions will work, try and see if you can upgrade or downgrade certain libraries to see if you can get better results.
Article about Mask R-CNN with video, I find it to be very useful and resourceful.
https://www.pyimagesearch.com/2018/11/19/mask-r-cnn-with-opencv/
The GitHub repo can be found below.
https://github.com/matterport/Mask_RCNN
EDIT
You can use this method across multiple cameras, just set up multiple video captures within a computer vision library like OpenCV. I assume this would be done with Python, which both Mask R-CNN and OpenCV are primarly based in.
I want to train Faster R-CNN network with my own images to detect faces. I have checked quite a few Github libraries, but this is the example of the training file I always find:
/data/imgs/img_001.jpg,837,346,981,456,cow
/data/imgs/img_002.jpg,215,312,279,391,cat
But I can't find an example how to train with images containing couple objects. Should it be:
1) /data/imgs/img_001.jpg,837,346,981,456,cow,215,312,279,391,cow
or
2) /data/imgs/img_001.jpg,837,346,981,456,cow
/data/imgs/img_001.jpg,215,312,279,391,cow
?
I just could not help myself but quote FarCry3 here: "The definition of insanity is doing the same thing over and over and expecting different results."
(Note that this is purely in an entertaining context, and not meant to insult you in any way; I would not take the time to answer your question if I didn't think it worthwile)
In your second example, you would feed the exact same input data, but require the network to learn two different outcomes. But, as you already noted, it is not very common for many of the libraries to support multiple labels per image.
Oftentimes, this is purely done for the sake of simplicity, as it requires you to change your metrics, to accomodate for multiple outputs: Instead of having one-hot encoded targets, you now could have multiple "targets".
This is even more challenging in the task of object detection (and not object classification, as described before), since you now have to decide how you represent your targets.
If it is possible at all, I would personally restrict myself to labeling one class per image, or have a look at another image library that does support that, since the effort of rewriting that much code is probably not worth the minute improvement in the results.
I need to visually recognise some flat pictures showed to camera. There are not many of them (maybe 30) but discrimination may depend on details. The input may be partly obscured or shadowed and is suspect to lighting changes.
The samples need to be updatable.
There are many existing frameworks for object detection, with the most reliable ones depending on deep learning methods (mostly convolutional networks). However, the pretrained models are not well optimised to discern flat imagery of course, and even if I start training from scratch, updating the system for new samples would take a cumbersome training process, if I am right about how this works.
Is it possible to use deep learning while still keeping the sample pool flexible?
Is there any other well known reliable method to detect images from a small sample set?
One can use well trained networks for visual classification like Inception or SqueezeNet, slice of the last layer(s) and add a simple statistical algorithm (for example k-nearest neighbour) that can be directly teached by the samples in a non-iterative fashion.
Most classification-related calculations like lighting and orientation insensitivity are already handled by the pre-trained network then, while the network's output keep enough information to allow statistical algorithms decide the image class.
An implementation using k-nearest neighbour is shown here: https://teachablemachine.withgoogle.com/ , the source is hosted here: https://github.com/googlecreativelab/teachable-machine .
Use transfer learning; you’ll still need to build a training set, but you’ll get better results than starting with random weights. Try to find a model trained on images similar to yours. You might also do some black box testing of the selected model with your curated images to baseline it’s response curve to your images.
Can machine learning be used to transform/modifiy a list of numbers.
I have many pairs of binary files read from vehicle ECUs, an original or stock file before the vehicle was tuned, and a modified file which has the engine parameters altered. The files are basically lists of little or big endian 16 bit numbers.
I was wondering if it is at all possible to feed these pairs of files into machine learning, and for it to take a new stock file and attempt to transform or tune that stock file.
I would appreciate it if somebody could tell me if this is something which is at all possible. All of the examples I've found appear to make decisions on data rather than do any sort of a transformation.
Also I'm hoping to use azure for this.
We would need more information about your specific problem to answer. But, supervised machine learning can take data with a lot of inputs (like your stock file, perhaps) and an output (say a tuned value), and learn the correlations between those inputs and output, and then be able to predict the output for new inputs. (In machine learning terminology, these inputs are called "features" and the output is called a "label".)
Now, within supervised machine learning, there is a category of algorithms called regression algorithms. Regression algorithms allow you to predict a number (sounds like what you want).
Now, the issue that I see, if I'm understanding your problem correctly, is that you have a whole list of values to tune. Two things:
Do those values depend on each other and influence each other? Do any other factors not included in your stock file affect how the numbers should be tuned? Those will need to be included as features in your model.
Regression algorithms predict a single value, so you would need to build a model for each of the values in your stock file that you want to tune.
For more information, you might want to check out Choosing an Azure Machine Learning Algorithm and How to choose algorithms for Microsoft Azure Machine Learning.
Again, I would need to know more about your data to make better suggestions, but I hope that helps.