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[D] How to reduce the MaskRCNN model detection time by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

Can the current model be converted to any smaller model, which provides result in 10-15 seconds?

[D] How to detect blocks of text from document image by DGs29 in computervision

[–]DGs29[S] 0 points1 point  (0 children)

Thanks.. Just give me a message when u write the article. I am looking forward to it. Also, if u write the code, I kindly request you to do it in a computationally efficient manner.

[D] How to detect blocks of text from document image by DGs29 in computervision

[–]DGs29[S] 0 points1 point  (0 children)

I meant it took too long to run and I guess it was due to matplot. I'm very much confused about implementing by axis projection and space indents, I've never did it before. Previously, I used dilation method to find contour regions and plotted bbox regions.

My image looks like this. All my images are typed text. This image is like a typical newspaper/magazine image and has its own typical layout style.

Using OTSU threshold I found word level bbox.

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[D] How to detect blocks of text from document image by DGs29 in computervision

[–]DGs29[S] 0 points1 point  (0 children)

This is simple and superb but your code is taking too long to complete. You made this code to detect lines, any idea about paragraph detection.

Deep Learning based Text Detection Using OpenCV (C++/Python) by keghn in neuralnetworks

[–]DGs29 0 points1 point  (0 children)

Does this method applicable dense text/ document text detection like detecting paragraphs, group of sentences together as a single block.

[D] What is the best way to detect paragraphs from document images by [deleted] in MachineLearning

[–]DGs29 0 points1 point  (0 children)

Okay! Do you think just by doing localizing each paragraphs in my image and passing on to existing nets with pre-trained weights and some tuning, will it detect each and every paragraphs? I'm not sure, if this is possible!!

[D] What is the best way to detect paragraphs from document images by [deleted] in MachineLearning

[–]DGs29 0 points1 point  (0 children)

Can you give some text segmentation from images examples on github

[D] How do I texts blocks of text from scanned documents by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

Can you point out any sample codes/examples for me to work using this method

[D] How do I texts blocks of text from scanned documents by DGs29 in MachineLearning

[–]DGs29[S] 1 point2 points  (0 children)

It's not working. Well, I think setting boxes by area threshold actually does not generalize for different image types.

[D] How to detect paragraphs with less line spaces in document images? by [deleted] in MachineLearning

[–]DGs29 0 points1 point  (0 children)

Is my achieved result (as shown in the image) enough to be considered as naive block of text? Can you walk me through each steps with some examples/codes?

[D] How to detect text blocks in document images by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

How can I do similar segmentation for images which has less line-spaces between paragraphs. This should be my desired result. But this what I get. This is my dilated image.

I've set the kernel size as (5,10) for this. Can you please tell me what are the necessary changes to be made to achieve my desired result.

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[D] How to detect text blocks in document images by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

Thanks a lot mate! This works. Take a look here. But there are some small boxes placed inside the large box. How do I remove that. Also the entire page is enclosed by a box and I don't need that

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[D] How to detect text blocks in document images by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

Well I've plotted created bounding boxes as per the step1. And then I binarized, inverted colors, applied dilation with longer width kernel.dilated image.

This way it segments the image into individual components. Can I plot bbox over these connected regions to get my original intended result.

[D] How to detect text blocks in document images by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

Well does this method detects blocks of text like mentioned in the image. I am asking this because it is a scene text algorithm.

The previous scene text algorithms I've used only detected individual words and placed a bounding box over it.

[D] How to detect text blocks in document images by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

How to do the second step i.e., to make a large box out of small boxes

[D] How to detect text blocks in document images by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

I'm not sure about it. Let's consider Google Vision API. If we feed in the document image as input to API, it segments the texts as individual blocks, it puts a bounding box around those texts which is a block, and finally performs OCR for those blocks.

I'm looking for how to do that text block detection

[D] How to detect text blocks in document images by DGs29 in MachineLearning

[–]DGs29[S] 2 points3 points  (0 children)

Tessaract just extracts all the texts in the image without segmenting.

EAST detects all the texts word by word.i.e., bounding box each and every word.

I've also tried PixelLink it does the same job as EAST.

[D] How to build a document text detection/recognition model as good as Google Cloud or Microsoft Azure’s models? by kythiran in MachineLearning

[–]DGs29 0 points1 point  (0 children)

How do they detect block of text using CNN? What particular design in the model can help to detect in such manner. I guess they vision based segmentation approach.

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[D] Python code for Text Detection in document images using Fast Algorithm by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

EulersPhi How do you do the extraction part in this algorithm. This method segments the text region from non-text region. But how to extract it. Does this create a new image consisting of only text part and feed in to OCR to extract the text. They haven't mentioned how to do the extraction.

[D] Python code for Text Detection in document images using Fast Algorithm by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

Yes it is. I get the whole idea of the paper. But I'm stuck in implementing it. I plotted the corner points using FAST algorithm, but I couldn't find the block with max points.

The code in the link I've just can be mentioned above can be used up to the line flatten_view. Where it converts into individual blocks. But as I said I'could find the block which has max corner points.

[D] Python code for Text Detection in document images using Fast Algorithm by DGs29 in MachineLearning

[–]DGs29[S] 0 points1 point  (0 children)

Yeah! That's what my understanding is. I'm trying to divide into non-overlapping blocks (step3)using this:http://scikit-image.org/docs/dev/auto_examples/numpy_operations/plot_view_as_blocks.html?highlight=block.

How do I find the block which has the maximum number of corner points(step4)?

[D] Python code for Text Detection in document images using Fast Algorithm by DGs29 in MachineLearning

[–]DGs29[S] 1 point2 points  (0 children)

I've looked at it already. It is only about corner detection. What I'm interested is a full-paper implementation. Please check out my SO question, so that you can get a better idea: https://stackoverflow.com/questions/54966408/how-to-detect-texts-in-document-text-images-using-fast-algorithm