<p>The cumulative number of versions of a single character that the human brain can recognize is nearly infinite, whereas computers have to be programmed to recognize every single variation. Humans also recognize 'context' in a word, whereas a computer does not recognize context because it does not have an intuitive understanding of language. From <a href="https://en.wikipedia.org/wiki/CAPTCHA" rel="nofollow noreferrer">wikipedia</a>:</p>
<blockquote>
<p>For example, when an individual understands that the first letter of a CAPTCHA is an “a”, that individual also understands where the contours of that “a” are, and also where it melds with the contours of the next letter. Additionally, the human brain is capable of dynamic thinking based upon context. It is able to keep multiple explanations alive and then pick the one that is the best explanation for the whole input based upon contextual clues. This also means it will not be fooled by variations in letters.</p>
</blockquote>
<p>Essentially it boils down to two factors: one, humans are preprogrammed with 'object recognition' abilities. Two, humans are able to parse a string of symbols in a way that conforms to the rules of a formal grammar.</p>
<p>Object recognition is the ability to find and recognize and identify human objects in a sequence. Humans naturally have this ability, whereas computers do not. It appears to be a genetic quality, and can be replicated in computers by <a href="https://en.wikipedia.org/wiki/Genetic_algorithm" rel="nofollow noreferrer">genetic algorithms</a> that mimic the process of natural selection.</p>
<p>Humans also build up sentences incrementally, and as they do so, they make constant unconscious predictions about what the word or sentence is going to say. This is the explanation behind <a href="https://en.wikipedia.org/wiki/Garden_path_sentence" rel="nofollow noreferrer">garden path sentences</a>. We can use event-related potentials to study this phenomenon in the human brain. Certain potentials are activated during specific situations when parsing. From the wikipedia page on garden-path sentences:</p>
<blockquote>
<p>Within ERPs, P600 is the most important component. Its activation occurs when the parser comes across a syntactic violation such as The broker persuaded to sell the stock or when parses synthesizes an unsatisfactory disambiguation on an ambiguous string of words such as The Doctor Charged the patient was lying. Hence the activation of P600 marks the parser's attempt to revise the sentence's structural mis-match or ambiguity.</p>
<p>It also appears that the presence of a disfluency in a sentence—caused by filled and long silent parses—does not elicit the P600. Instead, it elicits another ERP component, N400, which gets activated when people try to integrate a new word into the preceding sentence's context.</p>
</blockquote>
<p>Based on the above, it appears that the brain has specific event-related potentials that intuitively activate during specific situations while parsing. In other words, a human can recognize familiar sentences and words intuitively, and when it comes across an unrecognized word, it attempts to integrate it based on context. Since computers do not currently have a similar intuitive ability, CAPTCHAs are harder for them to decode.</p>

CC BY-SA 3.0
2015-05-11T11:11:26.090Z
Do you have any references for the information the wikipedia page? Anyone can write on there so peer-reviewed published work would be more reliable
queenslug
CC BY-SA 3.0
2015-05-14T12:15:06.880Z
There are some concerning aspects about this answer. Computers don't naturally lack any ability that humans do per se. that's a bit meaningless. Also, genetic algorithms don't have much to do with it, they are an optimisation routine, and human's ability to recognise objects in a sequence is not related to any "genetic quality". If you believe these things, I'd advise you to provide references, because I am not familiar wiht any such argument in the literature.
analystic
CC BY-SA 3.0
2015-05-10T19:03:20.207Z
2015-05-10T19:03:20.207Z
2020-06-17T09:55:31.633Z
Sydney Maples
<p><strong>A human brain recognises letters by their constituent features</strong> (sub-letter parts). It is modelized by a pandemonium model where printed information is extracted locally then globally. In the letter recognition literature, this type of feature-based hierarchical model competes with template matching theories (with an advantage to the pandemonium-like models such as deep-learning networks).</p>

<p><img src="https://i.sstatic.net/WvPvJ.png" alt="enter image description here"> </p>

<p>Scrambled characters are recognised the same way, with the difference that only part of the visual information is available. It relates to the theory of geons developed by Biedermann (Recognition by components, 1987). He wrote </p>

<blockquote>
  <p>if an arrangement of two or three sub-components can be recovered from
  the input, objects can be quickly recognized even when they are
  occluded or extensively degraded.</p>
</blockquote>

<p>Could you identify the following object? Left is the unrecoverable version. </p>

<p><img src="https://i.sstatic.net/WlQIL.png" alt="enter image description here"></p>

<p>REF:</p>

<ul>
<li>Grainger et al (2008). Letter perception: from pixels to pandemonium. Trends in Cognitive Science.</li>
<li>Biederman, Psychological Review (1987) &amp; <a href="http://en.wikipedia.org/wiki/Recognition-by-components_theory" rel="nofollow noreferrer">http://en.wikipedia.org/wiki/Recognition-by-components_theory</a></li>
</ul>

CC BY-SA 3.0
2015-06-18T20:14:17.517Z
Is the recognition by components theory and gestalt principle related? Are both applicable to my problem?
Sarthak Singhal
CC BY-SA 3.0
2015-06-19T11:41:09.847Z
You can see components as an extension of gestalt theory and both are applicable to your problem.
marsei
CC BY-SA 3.0
2015-05-11T23:23:16.983Z
2015-05-11T23:23:16.983Z
marsei
<p>So I'm doing a research on developing a new text-based CAPTCHA system. I've devised a scheme where characters in a text are broken/split individually and randomly, making it difficult for OCR machines to decode it. (See figure for example) This CAPTCHA is still under development, but it gives a rough idea of what I'm talking here.</p>

<p><img src="https://i.sstatic.net/o2Ds3.png" alt="CAPTCHA Example which reads &quot;NaAwKgkP&quot;"></p>

<p>My main aim here is to understand how the brain reads scrambled characters, as shown in the figure. I was looking up and a term called "filling-in" phenomena of vision came up. I'm not sure if that term is right in this context. </p>

<p><strong>I want to know the terminology and basic understanding of how humans are able to read scrambled text/characters easily.</strong></p>

CC BY-SA 3.0
2015-05-10T12:09:21.347Z
Welcome to CogSci. I've tried to clarify your question a bit. Related but probably not duplicate: http://cogsci.stackexchange.com/q/9992/2868
Christian Hummeluhr
CC BY-SA 3.0
2015-05-10T12:41:39.207Z
Hi, I have read that already, it doesn't specifically answer my question of how a broken text/letter is recognized by human brain easily. For example, the letter N in the above picture, is not displayed in its entirety but parts (being broken from in between); so, how the brain processes it as a whole and concludes it to be letter N?
Sarthak Singhal
CC BY-SA 3.0
2015-05-10T18:06:48.820Z
i'm sure there's also a neurological basis for it, but a good start would be to look into wertheimer's gestalt theory. some useful references are http://www.smashingmagazine.com/2014/03/28/design-principles-visual-perception-and-the-principles-of-gestalt/ and http://www.scholarpedia.org/article/Gestalt_principles
faustus
<cognitive-psychology><perception><cognitive-neuroscience><vision><artificial-intelligence>
How are humans able to read scrambled text/characters (e.g., CAPTCHA)?
Sarthak Singhal
# How are humans able to read scrambled text/characters (e.g., CAPTCHA)?

(Asked by: username_0 on 2015-05-10)

So I'm doing a research on developing a new text-based CAPTCHA system. I've devised a scheme where characters in a text are broken/split individually and randomly, making it difficult for OCR machines to decode it. (See figure for example) This CAPTCHA is still under development, but it gives a rough idea of what I'm talking here.

![CAPTCHA Example which reads ](https://i.sstatic.net/o2Ds3.png)

My main aim here is to understand how the brain reads scrambled characters, as shown in the figure. I was looking up and a term called "filling-in" phenomena of vision came up. I'm not sure if that term is right in this context.

**I want to know the terminology and basic understanding of how humans are able to read scrambled text/characters easily.**

**Tags:** cognitive-psychology, perception, cognitive-neuroscience, vision, artificial-intelligence

> **Comments:**
> **username_1**: Welcome to CogSci. I've tried to clarify your question a bit. Related but probably not duplicate: http://cogsci.stackexchange.com/q/9992/2868  
> **username_0**: Hi, I have read that already, it doesn't specifically answer my question of how a broken text/letter is recognized by human brain easily. For example, the letter N in the above picture, is not displayed in its entirety but parts (being broken from in between); so, how the brain processes it as a whole and concludes it to be letter N?  
> **username_2**: i'm sure there's also a neurological basis for it, but a good start would be to look into wertheimer's gestalt theory. some useful references are http://www.smashingmagazine.com/2014/03/28/design-principles-visual-perception-and-the-principles-of-gestalt/ and http://www.scholarpedia.org/article/Gestalt_principles  

---

## 2 answers

### Answer by username_6 on 2015-05-11. Score: 5 | Accepted answer

**A human brain recognises letters by their constituent features** (sub-letter parts). It is modelized by a pandemonium model where printed information is extracted locally then globally. In the letter recognition literature, this type of feature-based hierarchical model competes with template matching theories (with an advantage to the pandemonium-like models such as deep-learning networks).

![enter image description here](https://i.sstatic.net/WvPvJ.png)

Scrambled characters are recognised the same way, with the difference that only part of the visual information is available. It relates to the theory of geons developed by Biedermann (Recognition by components, 1987). He wrote

> if an arrangement of two or three sub-components can be recovered from
> the input, objects can be quickly recognized even when they are
> occluded or extensively degraded.

Could you identify the following object? Left is the unrecoverable version.

![enter image description here](https://i.sstatic.net/WlQIL.png)

REF:

* Grainger et al (2008). Letter perception: from pixels to pandemonium. Trends in Cognitive Science.
* Biederman, Psychological Review (1987) & <http://en.wikipedia.org/wiki/Recognition-by-components_theory>

> **Comments:**
> **username_0**: Is the recognition by components theory and gestalt principle related? Are both applicable to my problem?  
> **username_6**: You can see components as an extension of gestalt theory and both are applicable to your problem.  

---

### Answer by username_3 on 2015-05-10. Score: -1

The cumulative number of versions of a single character that the human brain can recognize is nearly infinite, whereas computers have to be programmed to recognize every single variation. Humans also recognize 'context' in a word, whereas a computer does not recognize context because it does not have an intuitive understanding of language. From [wikipedia](https://en.wikipedia.org/wiki/CAPTCHA):

> For example, when an individual understands that the first letter of a CAPTCHA is an “a”, that individual also understands where the contours of that “a” are, and also where it melds with the contours of the next letter. Additionally, the human brain is capable of dynamic thinking based upon context. It is able to keep multiple explanations alive and then pick the one that is the best explanation for the whole input based upon contextual clues. This also means it will not be fooled by variations in letters.

Essentially it boils down to two factors: one, humans are preprogrammed with 'object recognition' abilities. Two, humans are able to parse a string of symbols in a way that conforms to the rules of a formal grammar.

Object recognition is the ability to find and recognize and identify human objects in a sequence. Humans naturally have this ability, whereas computers do not. It appears to be a genetic quality, and can be replicated in computers by [genetic algorithms](https://en.wikipedia.org/wiki/Genetic_algorithm) that mimic the process of natural selection.

Humans also build up sentences incrementally, and as they do so, they make constant unconscious predictions about what the word or sentence is going to say. This is the explanation behind [garden path sentences](https://en.wikipedia.org/wiki/Garden_path_sentence). We can use event-related potentials to study this phenomenon in the human brain. Certain potentials are activated during specific situations when parsing. From the wikipedia page on garden-path sentences:

> Within ERPs, P600 is the most important component. Its activation occurs when the parser comes across a syntactic violation such as The broker persuaded to sell the stock or when parses synthesizes an unsatisfactory disambiguation on an ambiguous string of words such as The Doctor Charged the patient was lying. Hence the activation of P600 marks the parser's attempt to revise the sentence's structural mis-match or ambiguity.
>
> It also appears that the presence of a disfluency in a sentence—caused by filled and long silent parses—does not elicit the P600. Instead, it elicits another ERP component, N400, which gets activated when people try to integrate a new word into the preceding sentence's context.

Based on the above, it appears that the brain has specific event-related potentials that intuitively activate during specific situations while parsing. In other words, a human can recognize familiar sentences and words intuitively, and when it comes across an unrecognized word, it attempts to integrate it based on context. Since computers do not currently have a similar intuitive ability, CAPTCHAs are harder for them to decode.

> **Comments:**
> **username_4**: Do you have any references for the information the wikipedia page? Anyone can write on there so peer-reviewed published work would be more reliable  
> **username_5**: There are some concerning aspects about this answer. Computers don't naturally lack any ability that humans do per se. that's a bit meaningless. Also, genetic algorithms don't have much to do with it, they are an optimisation routine, and human's ability to recognise objects in a sequence is not related to any "genetic quality". If you believe these things, I'd advise you to provide references, because I am not familiar wiht any such argument in the literature.  

---

