My goal is to find the title screen from a movie trailer. I need a service where I can search a video for a string, then return the frame with that string. Pretty obscure, does anything like this exist?
e.g. for this movie, I'd scan for "Sausage Party" and retrieve this frame:
Edit: I found the cloudsight api which would actually work except cost is prohibitive # $.04 per call assuming I need to split the video into 1s intervals and scan every image (at least 60 calls per video).
No exact service that I can find, but you could attempt to do this yourself...
ffmpeg -i sausage_party.mp4 -r 1 %04d.png
/usr/local/bin/parallel --no-notice -j 8 \
/usr/local/bin/tesseract -psm 6 -l eng {} {.} \
::: *.png
This extracts one frame a second from the video file, and then uses tesseract to extract the text via OCR into files of the same name as the image frame (eg. 0135.txt. However your results are going to vary massively depending on the font used and the quality of the video file.
You'd probably find it cheaper/easier to use something like Amazon Mechanical Turk , especially since the OCR is going to have a hard time doing this automatically.
Another option could be implementing this service by yourself using the Scene Text Detection and Recognition module in OpenCV (docs.opencv.org/3.0-beta/modules/text/doc/text.html). You can take a look at this video to get an idea of how such a system would operate. As pointed out above the accuracy would depend on the font used in the movie titles, the quality of the video files, and the OCR.
OpenCV relies on Tesseract as the underlying OCR but, alternatively, you could use the text detection and localization functions (docs.opencv.org/3.0-beta/modules/text/doc/erfilter.html) in OpenCV to find text areas in the image and then employ a different OCR to perform the recognition. The text detection and localization stage can be done very quickly thus achieving real time performance would be mostly a matter of picking a fast OCR.
Related
I am working on a project where my task is to identify machine part by its part number written on label attached to it or engraved on its surface. One such example of label and engraved part is shown in below figures.
My task is to recognise 9 or 10 alphanumerical number (03C 997 032 D in 1st image and 357 955 531 in 2nd image). This seems to be easy task however I am facing problem in distinguishing between useful information in the image and rest of the part i.e. there are many other numbers and characters in both image and I want to focus on only mentioned numbers. I tried many things but no success as of now. Does anyone know the image pre processing methods or any ML/DL model which I should apply to get desired result?
Thanks in advance!
JD
You can use OCR to the get all characters from the image and then use regular expressions to extract the desired patterns.
You can use OCR method, like Tesseract.
Maybe, you want to clean the images before running the text-recognition system, by performing some filtering to remove noise / remove extra information, such as:
Convert to gray scale (colors are not relevant, aren't them?)
Crop to region of interest
Canny Filter
A good start can be one of this tutorial:
OpenCV OCR with Tesseract (Python API)
Recognizing text/number with OpenCV (C++ API)
I need to scan a handwritten font. I am using tesseract OCR v3.02 for that. I have trained my OCR using box files and adding dictionary words as well but still I am not able to get 100% accuracy.
I am trying to scan the following image
So far the text file i am obtaining is like this:
We each took baths before bed. Bigfoot was so large that he had to use the bathtub more like a sink trying to clean
up his best. he left a pool of water around the usa It was a mess and I mopped it and removed all of the fur
left behind before taking a bath of my Own. I have to admit it was cguite disgusting. “It”s cguite simple.” Me
assured her. “Mere, I”ll show you.”Iimmy‘s was the favorite burger joint among us kids. Sreat burgers. and
there were video games and pinball machines. Jimmy”s has been around since the fift"es. all our parents used to
go there when they were young. “‘Mot exactly.""Another blanket. Ben. please."“I really hope so. because I feel so
Any help to improve the the OCR?
Apologies for tagging this just ImageJ - it's a problem regarding MicroManager, a microscopy plugin for it and I thought this would be best.
I'd recently taken images for an important experiment using MicroManager (a recent version, though I cannot recall the exact number). The IT services at my institution have recently been having some networking problems and my saved preferences for the software had been erased. I'd got half way through my experiment when I realised that I'd saved my images as separate image files (three greyscale TIFFs plus metadata text files) instead of OME-TIFF iamge stacks.
All of my ImageJ macros for image processing rely on having a multiple channel image stack, so this is a bit of a problem. Is there any easy way in MicroManager (or ImageJ) to bulk convert these single channel greyscale images into the OME-TIFF image stack after the images have already been taken?
Cheers.
You can start with a macro like this one:
// Convert your images to a stack
run("Images to Stack", "name=Stack title=[] use");
// The stack will default the images to time points. Convert to channels
run("Stack to Hyperstack...", "order=xyczt(default) channels=3 slices=1 frames=1 display=Color");
// Export as OME-TIFF
run("Bio-Formats Exporter");
This is designed to reconstruct one dataset at a time (open 3 images, run the macro and export the OME-TIFF).
If you don't want any dialogs to show you can pass an output directory to the Bio-Formats exporter:
run("Bio-Formats Exporter", "save=/path/to/image.ome.tif export compression=Uncompressed");
For the output file name you can get the original image name in the macro with getTitle()
There is also a template example on iterating over all the files in a directory, if you want to completely automate the macro. However this may take some tweaking since you want to operate on your images 3 at a time.
Hope that helps!
After spending over 10 hours to compile tesseract using libc++ so it works with OpenCV, I've got issue getting any meaningful results. I'm trying to use it for digit recognition, the image data I'm passing is a small square (50x50) image with either one or no digits in it.
I've tried using both eng and equ tessdata (from google code), the results are different but both get guess 0 digits. Using eng data I get '4\n\n' or '\n\n' as a result most of the time (even when there's no digit in the image), with confidence anywhere from 1 to 99.
Using equ data I get '\n\n' with confidence 0-4.
I also tried binarizing the image and the results are more or less the same, I don't think there's a need for it though since images are filtered pretty good.
I'm assuming that there's something wrong since the images are pretty easy to recognize compared to even simplest of the example images.
Here's the code:
Initialization:
_tess = new TessBaseAPI();
_tess->Init([dataPath cStringUsingEncoding:NSUTF8StringEncoding], "eng");
_tess->SetVariable("tessedit_char_whitelist", "0123456789");
_tess->SetVariable("classify_bln_numeric_mode", "1");
Recognition:
char *text = _tess->TesseractRect(imageData, (int)bytes_per_pixel, (int)bytes_per_line, 0, 0, (int)imageSize.width, (int)imageSize.height);
I'm getting no errors. TESSDATA_PREFIX is set properly and I've tried different methods for recognition. imageData looks ok when inspected.
Here are some sample images:
http://imgur.com/a/Kg8ar
Should this work with the regular training data?
Any help is appreciated, my first time trying tessarect out and I could have missed something.
EDIT:
I've found this:
_tess->SetPageSegMode(PSM_SINGLE_CHAR);
I'm assuming it must be used in this situation, tried it but got the same results.
I think Tesseract is a bit overkill for this stuff. You would be better off with a simple neural network, trained explicitly for your images. At my company, recently we were trying to use Tesseract on iOS for an OCR task (scanning utility bills with the camera), but it was too slow and inaccurate for our purposes (scanning took more than 30 seconds on an iPhone 4 at a tremendously low FPS). At the end, I trained a neural-network specifically for our target font, and this solution not only beat Tesseract (it could scan stuff flawlessly even on an iPhone 3Gs), but also a commercial ABBYY OCR engine, which we were given a sample from the company.
This course's material would be a good start in machine learning.
I have a custom made web server running that I use for scanning documents. To activate the scanner and load the image on screen, I have a scan button that links to a page with the following image tag:
<img src="http://myserver/archive/location/name.jpg?scan" />
When the server receives the request for a ?scan file it streams the output of the following command, and writes it to disk on the requested location.
scanimage --resolution 150 --mode Color | convert - jpg:-
This works well and I am happy with this simple setup. The problem is that convert (ImageMagick) buffers the output of scanimage, and spits out the jpeg image only when the scan is complete. The result of this is that the webpage is loading for a long time with the risk of timeouts. It also keeps me from seeing the image as it is scanned, which should otherwise be possible because it is exactly how baseline encoded jpeg images show up on slow connections.
My question is: is it possible to do jpeg encoding without buffering the image, or is the operation inherently global? If it is possible, what tools could I use? One thought I had is separately encoding strips of eight lines, but I do not know how to put these chunks together. If it is not possible, is there another compression format that does allow this sort of pipeline encoding? My only restriction is that the format should be supported by the mainstream browsers.
Thanks!
You want to subdivide the image with a space-filling-curve. A sfc recursivley subivide the surface in smaller tiles and because of it's fractal dimension reduce the 2d complexity to a 1d complexity. When you have subdivide the image you can you use this curve to continously scan the image. Or you can use a BFS and some sort of an image-low-frequency-detail filter to continuously scan higher resolution of your image. You want to look for Nick's spatial index hilbert curve quadtree blog but I don't think you can put the tiles together with a jpg format (cat?). Or you can continously reduce the resolution?
scanimage --resolution [1-150] --mode Color | convert - jpg:-