In azure they have 2 option to detect text from image which is handwriting or OCR, but I don't get the confidence score, so I need to get the best one text result,
And what engine can handle which is the best/similar based on spelling OR confidence score?
I too tried Azure API and sometimes it is not giving results as expected.What score value are you getting.Also,I would suggest ,please compare this with Google Vision API
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There are many ways to detect similarity between 2 texts like Jaccard Index, TFIDF cosine similarity or sentence embedding. However all of them are refering to use case of 2 texts which are fully to be compared.
Here, I don't know how to call it but I add Subset Text Similarity Score, is to detect/calculate a score to see whether a small text is an extract from a bigger text.
For example, there is a big text (from a news)
Google Stadia, the company's cloud gaming service, will shut down on January 18 after the game failed to gain the traction with users the company had hoped for.
The cloud gaming service debuted through a closed beta in October 2018 and publicly launched in November 2019.
In spite of the fact that users are about to lose access to all of their titles and save on Stadia, many publishers share ways to keep playing their games on other platforms, reports The Verge.
Moreover, Google is also refunding all Stadia hardware purchased through the Google Store as well as all the games and add-on content purchased from the Stadia store.
the objective of the subset text similarity is to detect whether this small text is a subset (extract) from the bigger text above. The small text can have sentences not in the same order as the bigger text to be compared.
Example small text
On Stadia, users are will lose access to all of their titles and saves. all Stadia hardware purchased through the Google Store will be refunded.
For a small text above, the subset similarity score should be very high.
Is there some package or NLP method that can do this?
I have a bit of a complicated process in Power Automate. I'm trying to parse user uploaded screenshots and categorizing them into different variables. At first, it seemed that an obvious choice would be to build and train the AI Model but the only issue is that the data in the screenshots can vary (i.e. some images will contain more rows, some won't contain the relevant data, and the data can be located in different regions of the screenshot).
Some example of images, which a user can upload, are as follows: (i) Samsung 1 Metrics, (ii) Samsung 2 Metrics (iii) iPhone metrics
My attempt was to perform OCR on the uploaded screenshot and then do string parsing. Therefore, I tried attempting the following flow: Flow Diagram and specifically the substring parsing as:
Substring parsing
Basically, I'm performing OCR on the screenshot and then searching for a substring which corresponds to the values that I'm interested in. I'm unsure if this is the best way to do this as it isn't dynamic (i.e. I have to offset the substring index by a certain amount of characters). Any advice is greatly appreciated.
I believe you should be able to train a custom Form Processing model to extract the information you need. You can two different collections in your training dataset to have the model be able to recognize both Samsung and iPhone layouts.
All you'll need is 5 samples for each collection and you should be good to go.
Is there a way using Azure Cognitive Services to compare two images each containing handwritten
signatures or stamps to come back with a confidence level that the signatures in the scanned
images are similar or that the stamp is the right stamp?
Hint:
1- We are "not" looking for a solution to convert printed or handwritten text in an
image to a machine readable format (like OCR for example)
2- We want to compare two images together and come back with a confidence level that they
are similar. For example, in Face recognition the Face API is able to take two different
images of the face of a person and can identify it is the same person. The only difference
in our scenario is instead of dealing with Faces we want to deal with Handwritten
Signatures and Stamps.
This is not an available feature on Azure Cognitive Search. You'll need to work with some computer vision AI model.
I am developing an application that uses the Google vision API and I have a question about the colors properties.
Is the color shown in the properties with the highest percentage is the dominant color? And how that works ?
Because the color with highest percentage is not accurate.
What is the feature you are using?
I suggest you provide a sample image, the sample request and the responses you get so we know more about how to improve.
I am trying to develop my first ocr project using python. I am also new to Python as well.
I am using OpenCV 3, with Python 3.6. I am trying to do OCR from a document.
Using OpenCV I have converted image to white bg and black text. It is reading most of the image fine. But to improve accuracy, I would like to pass list of pattern to an OCR engine.
Is this possible with pytesseract ?
for example I am trying to extract customer Id, and there only few customer ids possible, I can provide list of those to improve accuracy.
Same with date, I know it is always in a specific date format and if I can provide date format to improve accuracy ...
Any of these is possible? If yes can any one suggest me how ?
I have tried to search on stack overflow and google but could not find much help.
any guidance or direction will be helpful ...