61–70 of 73 posts
Anyone know how this (EasyOCR) compares with a service like AWS Textract?
Cost: AWS is not free vs Open Sourced
Time: AWS averages under 10 seconds vs 140 seconds on a standard Dell 7480 & 9 seconds on a GPU Google colab
Character Accuracy: Almost same on a high quality input. No comparison with AWS on a blurred camera photo like this https://github.com/ExtractTable/ExtractTable-py/blob/master/...
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I'm just happy to see some advancement in open source OCR for Python. Last time I had a Python project that needed OCR, I found that the open-source options were surprisingly limited, and it required some effort to achieve consistently good results even with relatively clean inputs. Honestly I was kind of surprised that good basic OCR isn't a totally solved issue with an ecosystem of fully open-source solutions by no…
> Honestly I was kind of surprised that good basic OCR isn't a totally solved issue with an ecosystem of fully open-source solutions by now. Yes! Can anyone comment on why this is the case, since OCR is proclaimed to be a solved problem? I've always wondered why Google Lens works "out of the box" and shows great accuracy on extracting text from images taken using a phone camera, but open-source OCR software (Tesserac…
We were basically trying to transcribe message screenshots which should have been relatively straightforward given the homogeneity of the font. But this was not the case as tesseract was not trained in the layout of msg screenshots. The accuracy of raw tesseract on our test dataset was somehwere about 0.5-0.6 BLEU.
Once we were able to isolate individual parts of the image and feed it to tesseract, we were able to get around 0.9 BLEU on the same dataset.
TLDR;Some nifty image processing is required to make tesseract perform as expected.
[0] (https://www.askgoose.com) [1] (https://github.com/tesseract-ocr/tesseract)
Compared to ABBYY, how does this thing fare? I don't have the time right now to do this test and if anyone here did it I'd be thankful to share.
It works for sparse text on images, and for that specific use case it is better than Tesseract.
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I followed the resources here: https://github.com/tesseract-ocr/tessdoc/blob/master/Trainin... Also this: https://github.com/UB-Mannheim/tesseract/wiki The original data was here: https://github.com/tesseract-ocr/langdata_lstm I did use another data source from Manheim but can't locate it right now. Using vanilla Ubuntu 18.04 I looked at the example training files and made a small script to convert my own labeled dat…
If you don't mind disclosing, what was your particular use-case (the labeled dataset you trained on)?
Compared to ABBYY, how does this thing fare? I don't have the time right now to do this test and if anyone here did it I'd be thankful to share.
It can not compete with cloud services from Abbyy, Google, OCR.space and others. But it runs locally and is open-source. It works for sparse text on images, and for that specific use case it is better than Tesseract.
From what I can tell (without having read the research papers) it looks like this is just an easy to use package for sparse scene text extraction. It seems to do okay if the scene has sparse text but it falls down for dense text detection. The results are going to be pretty bad if you try and do a task like "extract transactions from a picture of a receipt." Here's an example of input you might get for a production a…
I'm just happy to see some advancement in open source OCR for Python. Last time I had a Python project that needed OCR, I found that the open-source options were surprisingly limited, and it required some effort to achieve consistently good results even with relatively clean inputs. Honestly I was kind of surprised that good basic OCR isn't a totally solved issue with an ecosystem of fully open-source solutions by no…
I'm surprised, too. After all, if you can train an AI to recognize a cat, why can't it be trained to recognize a letter?
Mine, for example, works well on clean laser-printed text. It fails on anything written with a typewriter, though. (My definition of "failure" is it's quicker to retype it from scratch than fix the OCR's errors.)
I'd also love to have one that worked on cursive handwriting.
From what I can tell (without having read the research papers) it looks like this is just an easy to use package for sparse scene text extraction. It seems to do okay if the scene has sparse text but it falls down for dense text detection. The results are going to be pretty bad if you try and do a task like "extract transactions from a picture of a receipt." Here's an example of input you might get for a production a…
I'm just happy to see some advancement in open source OCR for Python. Last time I had a Python project that needed OCR, I found that the open-source options were surprisingly limited, and it required some effort to achieve consistently good results even with relatively clean inputs. Honestly I was kind of surprised that good basic OCR isn't a totally solved issue with an ecosystem of fully open-source solutions by no…
From what I can tell (without having read the research papers) it looks like this is just an easy to use package for sparse scene text extraction. It seems to do okay if the scene has sparse text but it falls down for dense text detection. The results are going to be pretty bad if you try and do a task like "extract transactions from a picture of a receipt." Here's an example of input you might get for a production a…
About a year ago I surveyed the available OCR packages for receipts. This was for pristine scans (not the crumpled scan you have in your image). In my survey all OCRs failed except google cloud OCR! If there is another OCR that works I would love to know.
In my job as a support engineer I sometimes get screenshots of complex technical configurations and end up having to type them in one character at a time, so this would be really handy.
Looks like maybe I could just create a wrapper around EasyOCR.
