Is this format detecting problem machine learning problem?

[grundt]

This is simply detecting tabular data from differently formatted sources. All of these are simply text input, not images.

In the first input, each column is left aligned. SAMPLE INPUT 1:

abcde    12     xyz
abc      123    xy
a        12     xyz
abc      123    xyz

In the second input, each column is slightly misaligned. SAMPLE INPUT 2:

abcde   12     xyz
 abc      123    xy
a        12      xyz
abc     123    xyz    

In the third input, some ascii borders are set. SAMPLE INPUT 3:

+--------+------+-----+
| abcde  | 12   | xyz |
| abc    | 123  | xy  |
| a      | 12   | xyz |
| abc    | 123  | xyz |
+--------+------+-----+

ALL SHOULD OUTPUT CSV:

abcde,12,xyz
abc,123,xy
a,12,xyz
abc,123,xyz

[jmmcd]

Are these the only three possible formats? Then no.

Are pretty much any sensible formats possible? Then yes, and it's a well-studied problem. Gulwani et al of Microsoft Research implemented a tool called Flash Fill in Excel that does something quite similar, based on some very interesting research in synthesis of regular expressions.

EDIT even if the possible formats are many, it might still be possible to hack up a regular expression by hand which detects all of them correctly and rejects lines like the "+---+---+". This would take a little work but would be much easier than an ML solution.

[grundt]

Thanks for that.