1A Fast Method for Identifying Plain Text Files
2==============================================
3
4
5Introduction
6------------
7
8Given a file coming from an unknown source, it is sometimes desirable
9to find out whether the format of that file is plain text.  Although
10this may appear like a simple task, a fully accurate detection of the
11file type requires heavy-duty semantic analysis on the file contents.
12It is, however, possible to obtain satisfactory results by employing
13various heuristics.
14
15Previous versions of PKZip and other zip-compatible compression tools
16were using a crude detection scheme: if more than 80% (4/5) of the bytes
17found in a certain buffer are within the range [7..127], the file is
18labeled as plain text, otherwise it is labeled as binary.  A prominent
19limitation of this scheme is the restriction to Latin-based alphabets.
20Other alphabets, like Greek, Cyrillic or Asian, make extensive use of
21the bytes within the range [128..255], and texts using these alphabets
22are most often misidentified by this scheme; in other words, the rate
23of false negatives is sometimes too high, which means that the recall
24is low.  Another weakness of this scheme is a reduced precision, due to
25the false positives that may occur when binary files containing large
26amounts of textual characters are misidentified as plain text.
27
28In this article we propose a new, simple detection scheme that features
29a much increased precision and a near-100% recall.  This scheme is
30designed to work on ASCII, Unicode and other ASCII-derived alphabets,
31and it handles single-byte encodings (ISO-8859, MacRoman, KOI8, etc.)
32and variable-sized encodings (ISO-2022, UTF-8, etc.).  Wider encodings
33(UCS-2/UTF-16 and UCS-4/UTF-32) are not handled, however.
34
35
36The Algorithm
37-------------
38
39The algorithm works by dividing the set of bytecodes [0..255] into three
40categories:
41- The white list of textual bytecodes:
42  9 (TAB), 10 (LF), 13 (CR), 32 (SPACE) to 255.
43- The gray list of tolerated bytecodes:
44  7 (BEL), 8 (BS), 11 (VT), 12 (FF), 26 (SUB), 27 (ESC).
45- The black list of undesired, non-textual bytecodes:
46  0 (NUL) to 6, 14 to 31.
47
48If a file contains at least one byte that belongs to the white list and
49no byte that belongs to the black list, then the file is categorized as
50plain text; otherwise, it is categorized as binary.  (The boundary case,
51when the file is empty, automatically falls into the latter category.)
52
53
54Rationale
55---------
56
57The idea behind this algorithm relies on two observations.
58
59The first observation is that, although the full range of 7-bit codes
60[0..127] is properly specified by the ASCII standard, most control
61characters in the range [0..31] are not used in practice.  The only
62widely-used, almost universally-portable control codes are 9 (TAB),
6310 (LF) and 13 (CR).  There are a few more control codes that are
64recognized on a reduced range of platforms and text viewers/editors:
657 (BEL), 8 (BS), 11 (VT), 12 (FF), 26 (SUB) and 27 (ESC); but these
66codes are rarely (if ever) used alone, without being accompanied by
67some printable text.  Even the newer, portable text formats such as
68XML avoid using control characters outside the list mentioned here.
69
70The second observation is that most of the binary files tend to contain
71control characters, especially 0 (NUL).  Even though the older text
72detection schemes observe the presence of non-ASCII codes from the range
73[128..255], the precision rarely has to suffer if this upper range is
74labeled as textual, because the files that are genuinely binary tend to
75contain both control characters and codes from the upper range.  On the
76other hand, the upper range needs to be labeled as textual, because it
77is used by virtually all ASCII extensions.  In particular, this range is
78used for encoding non-Latin scripts.
79
80Since there is no counting involved, other than simply observing the
81presence or the absence of some byte values, the algorithm produces
82consistent results, regardless what alphabet encoding is being used.
83(If counting were involved, it could be possible to obtain different
84results on a text encoded, say, using ISO-8859-16 versus UTF-8.)
85
86There is an extra category of plain text files that are "polluted" with
87one or more black-listed codes, either by mistake or by peculiar design
88considerations.  In such cases, a scheme that tolerates a small fraction
89of black-listed codes would provide an increased recall (i.e. more true
90positives).  This, however, incurs a reduced precision overall, since
91false positives are more likely to appear in binary files that contain
92large chunks of textual data.  Furthermore, "polluted" plain text should
93be regarded as binary by general-purpose text detection schemes, because
94general-purpose text processing algorithms might not be applicable.
95Under this premise, it is safe to say that our detection method provides
96a near-100% recall.
97
98Experiments have been run on many files coming from various platforms
99and applications.  We tried plain text files, system logs, source code,
100formatted office documents, compiled object code, etc.  The results
101confirm the optimistic assumptions about the capabilities of this
102algorithm.
103
104
105--
106Cosmin Truta
107Last updated: 2006-May-28
108