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Ionic OCR demo

48 points by matiastucci 10y ago

rememberlenny 10y ago
This is a application of ocrad.js [1]. [1] https://github.com/antimatter15/ocrad.js https://github.com/antimatter15/ocrad.js
- matiastucci 10y ago
  yup, that's what the readme says
j_s 10y ago
Screenshots please, people! (Video probably could've been a .GIF and thus embedded in the README.)
siliconc0w 10y ago
You can also use http://tesseract.projectnaptha.com/ http://tesseract.projectnaptha.com/
- bijection 10y ago
  In fact antimatter15, who made ocrad.js, is a coauthor of tesseract.js! Javascript OCR is a small world.
partiallogic 10y ago
I will jump on this project as an example of PWA using ionic2, thanks
- matiastucci 10y ago
  great! happy coding
WalterGR 10y ago
Does this do layout detection?
Hydraulix989 10y ago
It's not THAT impressive. ocrad.js is just Tesseract, an ancient OCR engine from Google (EDIT: originally from HP). For bounded-length sequences of small alphabet sizes (e.g. digits 0-9 only), it's possible to use a convolutional neural network to get human-level 98+% accuracy (see Google's street view house numbers paper). Even for general purpose OCR, I personally built an OCR engine from scratch in 2013 using convolutional neural networks and cuDNN that readily beat Tesseract and was competitive with ABBYY (both use ad hoc methods). Character-level segmentation and classification (with distractors) can get you pretty far with enough training data. It did take me 6 months of time though, and I had some help along the way...
- avmich 10y ago
  It would be really interesting to learn how you did that. https://github.com/tesseract-ocr/docs/blob/master/das_tutorial2016/6ModernizationEfforts.pdf https://github.com/tesseract-ocr/docs/blob/master/das_tutori... "Despite being designed over 20 years ago, the current Tesseract classifier is incredibly difficult to beat with so-called modern methods. (Without changing features or upping the number of training fonts) Why?"
  - Hydraulix989 10y ago
    Character classification is the easy part of OCR. EDIT: It sounds like they were benchmarking a very constrained problem as well (only a single font, with certain features). One disadvantage with Tesseract is that it has to be trained by the user for specific fonts, my OCR was generalist (general shape-based character classification is practically designed for convolutional neural networks). I'd love to give more details in a blog post soon.
- pilooch 10y ago
  tesseract 4 comes with LSTM and pre trained models.
  - avmich 10y ago
    https://github.com/tesseract-ocr/tesseract/blob/master/README.md https://github.com/tesseract-ocr/tesseract/blob/master/READM... "The latest stable version is 3.04.01, released in February 2016." Was version 4 ever released?

48 points by matiastucci 10y ago

This is a application of ocrad.js [1]. [1] https://github.com/antimatter15/ocrad.js https://github.com/antimatter15/ocrad.js

matiastucci 10y ago
yup, that's what the readme says

j_s 10y ago

Screenshots please, people! (Video probably could've been a .GIF and thus embedded in the README.)

siliconc0w 10y ago

You can also use http://tesseract.projectnaptha.com/ http://tesseract.projectnaptha.com/

bijection 10y ago
In fact antimatter15, who made ocrad.js, is a coauthor of tesseract.js! Javascript OCR is a small world.

partiallogic 10y ago

I will jump on this project as an example of PWA using ionic2, thanks

matiastucci 10y ago
great! happy coding

WalterGR 10y ago

Does this do layout detection?

Hydraulix989 10y ago

It's not THAT impressive. ocrad.js is just Tesseract, an ancient OCR engine from Google (EDIT: originally from HP). For bounded-length sequences of small alphabet sizes (e.g. digits 0-9 only), it's possible to use a convolutional neural network to get human-level 98+% accuracy (see Google's street view house numbers paper). Even for general purpose OCR, I personally built an OCR engine from scratch in 2013 using convolutional neural networks and cuDNN that readily beat Tesseract and was competitive with ABBYY (both use ad hoc methods). Character-level segmentation and classification (with distractors) can get you pretty far with enough training data. It did take me 6 months of time though, and I had some help along the way...

avmich 10y ago
It would be really interesting to learn how you did that. https://github.com/tesseract-ocr/docs/blob/master/das_tutorial2016/6ModernizationEfforts.pdf https://github.com/tesseract-ocr/docs/blob/master/das_tutori... "Despite being designed over 20 years ago, the current Tesseract classifier is incredibly difficult to beat with so-called modern methods. (Without changing features or upping the number of training fonts) Why?"
- Hydraulix989 10y ago
  Character classification is the easy part of OCR. EDIT: It sounds like they were benchmarking a very constrained problem as well (only a single font, with certain features). One disadvantage with Tesseract is that it has to be trained by the user for specific fonts, my OCR was generalist (general shape-based character classification is practically designed for convolutional neural networks). I'd love to give more details in a blog post soon.
pilooch 10y ago
tesseract 4 comes with LSTM and pre trained models.
- avmich 10y ago
  https://github.com/tesseract-ocr/tesseract/blob/master/README.md https://github.com/tesseract-ocr/tesseract/blob/master/READM... "The latest stable version is 3.04.01, released in February 2016." Was version 4 ever released?