8
votes
Fake News Detection problem
This is a very ambitious project. First it's important to realize that ML cannot really solve this kind of problem in general, it can only help detect the posts which are likely fake news (see for ...
- 25.9k
8
votes
How can you build a model that extracts data out from receipts?
The simplest pipeline would be to do the following:
OCR
Named Entity Extraction
Entity Disambiguation
OCR
This is basically transforming your receipts into plain text.
If you have scans (pictures) ...
- 3,608
5
votes
Accepted
Document Layout Analysis - state-of-the-art?
After some research, I came across ICDAR (International Conference on Document Analysis and Recognition), which is taking place biannually and seems to be the most complete and up-to-date source for ...
- 211
4
votes
How to improve OCR (Scanning) results?"
Build a dictionary of common words that frequently appear in these documents (e.g., MEDICAL, SEX, AGE, etc). Then, for each word in the output from OCR, check whether it is similar to a word in your ...
- 3,411
3
votes
Evaluating information extraction from structured documents
I have worked with Structured text using OCR. OCR is prone to errors even while reading the content and a slight change in string arrangement would lead to false positives. I used Cosine Similarity ...
- 369
3
votes
How to pass features extracted using CNN into RNN?
1 and 2. You are in the right direction, you need to extract the features using a CNN, then instead of predicting the class you want to reshape the last layer of features and feed it directly into the ...
- 8,038
3
votes
How are OCR training datasets constructed?
OCR used to recognize letters uses a different training dataset than OCR for digits. One that contains, well, letters.
Similarly, if you have to recognize a number with a lot of digits, you simply ...
- 636
2
votes
Accepted
how can I solve label shape problem in tensorflow when using one-hot encoding?
Since your data are already in a one-hot encoding, you can use tf.nn.softmax_cross_entropy_with_logits(), which expects an input of shape ...
- 136
2
votes
Accepted
what is the loss function in char recognition using Tensorflow?
The loss function is correct, you just need to convert categorical variables into numerical representations using one-hot vector encoding. Please take a look at this.
- 64
2
votes
Accepted
Can I train two stacked models end-to-end on different resolutions?
I would suggest you to split your problem into two:
How to train?
How to make inference when resources are limited?
Very common pattern is to train model on large data (by renting AWS server, for ...
- 186
2
votes
Extract text from a image - OCR
I have used tesseract for similar tasks. I can give you few recommendation. You can choose the best that works for you.
Extract the parameter values by finding the exact locations they appear.
If ...
- 181
2
votes
Accepted
training neural nets for OCR
CTC can also work with totally random text (and thus without any "word pattern").
I know that because I trained an OCR network with CTC loss on random text which was then able to read non random text (...
- 322
2
votes
optical chemical structure recognition from images
What way I could follow to achieve this?
Create a dataset with image-label pairs.
Create a classification model.
I don't think ...
- 3,608
2
votes
how can i detect medicine name and info(use and contents) by using medicine wrappers
For data, you can search on IEEE Dataport , Kaggle.
For detecting medicine names and other info trained deep learning models like CNN or you can also perform fine-tuning from the existing model.
- 171
2
votes
Accepted
Pretrained handwritten OCR model
Discover open-source deep learning code and pretrained models at Model Zoo
These are pre-trained sources available in the Github.
Handwritten Text Recognition with TensorFlow
More
Handwriting OCR
...
- 4,088
2
votes
Accepted
extracting data from unstructured pdfs
This can be easily done using NLP and Computer Vision. These types of tasks usually come under Document Understanding domain.
For your problem statement you will need to create a Pipeline with 2 majot ...
- 2,165
2
votes
How to extract handwritten phone numbers from a huge set of documents?
In my opinion, Document Classification could be a good approach, but I think it's vague. There are much better options now. One approach you can think of is that, ...
- 1,010
2
votes
text extraction from bank statements from pdf format
I think the most convenient way to do that in Python is by using a tabula library. I happened to do similar task with pdf files and probably the easiest way to do so is to use tabula.read_pdf function....
- 660
1
vote
extracting data from unstructured pdfs
For each of the 20 designs, may have to design custom annotated section extractor using Vision AI algos. Then do OCR using tessaract or other OCR libraries on extracted sections.
I am not sure if this ...
1
vote
LSTM layer (keras) is causing all layers after it to constantly predict the same thing no matter the input
I wonder what you mean by "Everything after (and including) the 1st LSTM layer outputs the same value"?
It's not technically possible for that to be true and for the loss to be changing. ...
- 578
1
vote
How to start with receipt OCR text detection
Not sure if you tried Tesseract or pytesseract yet. I never worked on receipts but I had a different use case where I needed to segment images before I applied the tesseract for text recognition. For ...
- 11
1
vote
How to detect medicine name from the medicine wrapper
The Python Library
Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and “read” the text embedded in images. Python-tesseract is a wrapper for ...
- 111
1
vote
License Plate OCR
Intel OpenVINO is used to optimise inference speed on edge devices. One of their demos was license plate detection. The link can be found here: https://docs.openvinotoolkit.org/2019_R1/...
- 422
1
vote
How to create a model to recognize matching label and ROI with OCR
There are multiple ways to do it. Here is one technique:
Run OCR on your image and search for your desired text . in this case it is Total.
Get the pixels of that word - run ...
- 106
1
vote
How to segment old digitized newspapers into articles
As far as I know topic segmentation is not a particularly easy task with clean data, so it's likely to be challenging with noisy old French.
It's not exactly the same problem so I'm not sure if this ...
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1
vote
Does CRNN use sparse tensor value for its label?
My assumption here is that if the input vectors are of variable length, you should use something like keras.preprocessing.sequence.pad_sequences (as well as a masking layer to ignore these padded ...
- 171
1
vote
Extracting text from few areas on product label
If the image will always be the same and your ROI will always be in the same location, then an RNN with CV2 is not necessary, you can mark the areas that you need to extract the texts. I even ...
1
vote
Accepted
Machine learning algorithm which gives multiple outputs from single input
Keras functional API's are a way your can solve you problem. Using keras functional API, we can build models that resembles more like graphs such as this:
In order to build a model like this, you ...
- 2,435
1
vote
How can I split an image into rectangles?
AWS Rekognition works very well for detecting typewritten text but not so well for detecting hand written symbols. You can see this is true in your example. What I would do is detect all the boxes ...
- 9,056
1
vote
How can you build a model that reads out receipts and invoices?
You should start by trying OCR. I am not sure why you are rejecting it without trying it. Start with Tesseract (free but not very good) and then try a commercial OCR as well (Abbyy is well regarded, ...
- 3,411
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