22 votes
Accepted

Why can't OCR be perceived as a good example of AI?

Whenever a problem becomes solvable by a computer, people start arguing that it does not require intelligence. John McCarthy is often quoted: "As soon as it works, no one calls it AI anymore" (...
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  • 1,174
14 votes

Why can't OCR be perceived as a good example of AI?

Although OCR is now a mainstream technology, it remains true that none our methods genuinely have the recognition facilities of a 5 year old (claimed success with CAPTCHAs notwithstanding). We don't ...
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6 votes
Accepted

What are the challenges for recognising the handwritten characters?

Handwritten digit recognition is a standard benchmark in Machine Learning in the form of the MNIST dataset. For example, scikit-learn, a python package for Machine Learning uses it as a tutorial ...
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  • 1,132
5 votes
Accepted

Are there any textual CAPTCHA challenges which can fool AI, but not human?

It's an interesting question about what makes humans unique. There is a good book on the subject titled What Computers Cant Do by Hubert Dreyfus. One task that a computer can't handle (for now at ...
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  • 116
5 votes

Are there any textual CAPTCHA challenges which can fool AI, but not human?

A method that could possibly work is utilising optical illusions such as one where two lines down a hallway are identical but one seems longer to the human eye, then they could be prompted with a ...
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4 votes

Is it possible to use AI to denoise noisy documents?

This is also the topic of Image Processing (which has analytical solutions instead of learning) mostly through predesigned filters. The filter depends on the type of noise, (salt & pepper, ...
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  • 41
4 votes

Effective algorithms for OCR

Last semester I, along with my team, made a project on OCR. Note: I am assuming the data set for your pictures has white background with black (or some other dark text) on it. These are the overview ...
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  • 1,953
4 votes

Why can't OCR be perceived as a good example of AI?

I'm not sure if predicting MNIST can be really considered as an AI task. AI problems can be usually framed under the context of an agent acting in an environment. Neural nets and machine learning ...
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  • 41
2 votes

Are there any textual CAPTCHA challenges which can fool AI, but not human?

Have the user label highlighted objects in video that a state of the art classifier cannot solve Create a state of the art video classifier. Might as well train it on Google's YouTube-8M video ...
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  • 1,837
2 votes

How could I use machine learning to detect text and non-text regions in scanned documents?

TextDetector, Tesseract and other open source packages implement text detection (object detection for text). There's also a pretrained Tensorflow model that does text detection. A text detector will ...
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  • 151
2 votes
Accepted

Generate credit cards dataset for locating number region

I am assuming the question you are asking is how to prevent over-fitting on the maximum accuracy. Your graph does show that your model over-fits. There is a couple of different methods to prevent ...
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  • 243
2 votes
Accepted

In OCR, how should I deal with the warped text on the sides of oval objects?

There are many papers on this but the following is a good start: How to unwrap wine labels programmatically. The author includes source code in Python. You mentioned you do not want to do a ...
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2 votes
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Which AI techniques are there that combine multiple models to make sense of data at different stages?

You have explained a pipeline of AI algorithms for text in images: 1) Text detection, 2) OCR, 3) named entity recognition (NER). There are reams of paper on these topics. Extracting City and Country ...
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1 vote
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Why object detection algorithms are poor in optical character recognition?

Good question! Using Yolo to recognise characters would be a good experiment to try. It may be because of the density of characters on a page -- systems like Yolo are very good at detecting a small ...
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  • 391
1 vote

What are the disadvantages to using a distance metric in character recognition prediction

As I see it, the question boils down to the comparison between distance (function/metric) based Optical Character Recognition (OCR) and (for example) OCR done by means of Convolutional Neural Networks ...
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  • 707
1 vote
Accepted

How can I detect diagram region and extract (crop) it from a research paper

I will consider that you need to extract(crop) the digram from the pdf research paper. You can use PyPDF2 or PyMuPDF to extract the images from the PDF file and then you can apply machine learning to ...
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1 vote

Is there any way to classify Document Image without OCR?

It is possible to classify invoice scans without passing through an OCR component if they are visually different (they demonstrate different visual features). On the other hand, if the invoices look ...
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1 vote

How can we recognise musical notes in low-resolution or blurry images?

any subjective ideas or comments are more than welcome Not a complete answer, but some ideas: Your goal is subdivided into many tasks. It's not exactly the same as OCR because you also need to find ...
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1 vote

Effective algorithms for OCR

An interesting question, I think the algorithm used for OCR is "Logistic Regression" or "Decision Tree" in multiple steps. The steps can be Image Classification - In this step, the images are ...
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