4 ms·

Don't know where the assessment "Msft, Google APIs accuracy still below par" came from. In fact this is just comparing 3 APIs. In fact the state of the art of

by Isamu 9y ago

Don't know where the assessment "Msft, Google APIs accuracy still below par" came from. In fact this is just comparing 3 APIs. In fact the state of the art of OCR "in the wild" (using images off the street, for example) is far from 100%. Google Cloud Vision does pretty good. The ICDAR 2017 challenges (especially Robust Reading Challenges) should give you an idea of where we are now: http://u-pat.org/ICDAR2017/program_competitions.php http://u-pat.org/ICDAR2017/program_competitions.php

gajju3588 9y ago
How do i read this page, it seems to be listing some competitions.
- Isamu 9y ago
  Click through to the competitions. My point was that here are current competitions giving you an idea of the state of the art. These are not student competitions or top coder things. For example the Robust Reading on COCO text: http://rrc.cvc.uab.es/?ch=5 http://rrc.cvc.uab.es/?ch=5
Livven 9y ago
Current state-of-the-art approaches in this field are significantly better than existing commercial solutions at recognizing text in the wild (i.e. scene text). As an example, see the ICDAR 2015 results [1], where the Google Vision API is at 59.60% (Hmean) while the best ones are over 80%. Note that this test is about localization, i.e. finding the text location without recognizing the actual content, though on a more challenging dataset. As for recognition, see the table on page 6 of this paper [2]. The "IIIT5K None" column should be pretty close to what was done in the OP, using the same dataset, with recognition accuracies of around 80% while the Google Vision API is at 322/500=64.4%. Note here that since this paper is only about recognition, there is no localization step before which would otherwise act as a filter and decrease the accuracy a bit by failing to localize some text that the recognition step would be able to recognize. [1] http://rrc.cvc.uab.es/?ch=4&com=evaluation&task=1 http://rrc.cvc.uab.es/?ch=4&com=evaluation&task=1 [2] https://arxiv.org/pdf/1603.03915.pdf https://arxiv.org/pdf/1603.03915.pdf

by Isamu 9y ago

gajju3588 9y ago

How do i read this page, it seems to be listing some competitions.

Isamu 9y ago
Click through to the competitions. My point was that here are current competitions giving you an idea of the state of the art. These are not student competitions or top coder things. For example the Robust Reading on COCO text: http://rrc.cvc.uab.es/?ch=5 http://rrc.cvc.uab.es/?ch=5

Livven 9y ago

Current state-of-the-art approaches in this field are significantly better than existing commercial solutions at recognizing text in the wild (i.e. scene text). As an example, see the ICDAR 2015 results [1], where the Google Vision API is at 59.60% (Hmean) while the best ones are over 80%. Note that this test is about localization, i.e. finding the text location without recognizing the actual content, though on a more challenging dataset. As for recognition, see the table on page 6 of this paper [2]. The "IIIT5K None" column should be pretty close to what was done in the OP, using the same dataset, with recognition accuracies of around 80% while the Google Vision API is at 322/500=64.4%. Note here that since this paper is only about recognition, there is no localization step before which would otherwise act as a filter and decrease the accuracy a bit by failing to localize some text that the recognition step would be able to recognize. [1] http://rrc.cvc.uab.es/?ch=4&com=evaluation&task=1 http://rrc.cvc.uab.es/?ch=4&com=evaluation&task=1 [2] https://arxiv.org/pdf/1603.03915.pdf https://arxiv.org/pdf/1603.03915.pdf