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Female 1000 System

 Contents..

The first question is: Why have an image for each number from 000 to 999 (one thousand images)?

It is so that one imagined image can represent a three digit number - and that might be helpful for memorising most of a 4 digit history date; or for learning some kind of numeric password.

The next question is: If I am going to go to all that effort, is there any bonus benefit to learning so many images:numbers?

It is generally claimed that, when learning a foreign language, the most frequently used 2000 words are a good investment of a student's time. I was thinking that, if I could have two 1000 image systems then the images could not just represent 000-999 but also represent common vocabulary meanings;

then, if I am learning a tricky foreign word (that sits within the 2000 list), I could imagine its representative image in a story which involves another memory image to do with how to spell the word. Eg. 'T.h.i.n.k' and the 'PE' of 'Pe.n.s.e.r' if learning French. It is just a small prompt but the mind is pretty good at using a small prompt and recalling the whole thing.

Where the method is weak is when a language has a prefix used so often that many many words all start with the same letters: the two letter prompt is not specific enough. Then, you have to think about a bespoke image for a prefix like 'prze'.

Also, the prompt can be a one letter image (see the Alphabet-related article) or a 3 letter image (see the Robots article later in the course).

There is a less and less taught maths technique called logarithms. As a curiosity, I wanted to embed some logarithm table data in the image of each person. A log table would typically let you look up a 3 digit number and discover a 4 digit+ number by running your finger down a reference table. I wanted to record the 4 digit number appropriate for a 000-999 number, if any; I wanted a way to look at antilogarithms too (which also involve looking up a 3 digit number to find a 4 digit+ number).

The idea was that the eyes, nose, etc. of a face each indicate a digit. So, a face could be 'translated' into a 4 digit number based on choice of eye, nose, etc..

The 1000 women images are in this article. There is a separate 1000 men article.


Below are how cartoon women are described by their features.

The clothes 0-9 choice is just the colour of the person's clothes. Any three digit number beginning with 0 uses the 0 colour on the clothing.

The clothes consist of a top half and a bottom half. The 2nd and 3rd digits of the 3 digit number represent the top half clothing choice and the bottom half clothing choice.

Facial features: wrinkles beside eyes, straight eyebrows, eye bags, angled eyebrows, nose mark, red cheeks, arched eyebrows, bowed eyebrows, freckles, reverse direction brows.

In this way, the first digit of the 4 digit 'answer' number is represented by one of 10 facial features.

Hair / hat is not a random decision by me. I developed a shorthand abbreviation for each word in the 1000 word vocabulary. The shorthand is either a letter and a digit or two letters followed by a digit. There are 26 hair / hat types to represent the first letter of that shorthand.

The colour of the hair represents the second letter of the shorthand or, if the shorthand is a letter and a digit, the colour represents the digit.

Eye colour can create variety in the 1000 people. I have decided to use blue, brown, green, light blue eyes - in a looping sequence from 000 upwards.

I used an antilog table to get 4 digits that are represented as facial effect, eyes choice, nose choice, mouth choice. (The Male 1000 people article is to do with a logarithm table.)

Skin colour follows a loop of 7 colours.

The name of the person is the vocabulary meaning. If an image represents the word 'lid' then Lid is that person's name - a bit unusual!.

Women 1000 Table


There are 1001 rows in the table from 000 to 1000 but row 1000 is an additional row in case a 1000 integer item needs representing. It is not log related.


Video of the Women 1000 or so images (as digit pieces which need assembling)

Women 000 to 1000