1 A Fast Method for Identifying Plain Text Files
   2 ==============================================
   3 
   4 
   5 Introduction
   6 ------------
   7 
   8 Given a file coming from an unknown source, it is sometimes desirable
   9 to find out whether the format of that file is plain text.  Although
  10 this may appear like a simple task, a fully accurate detection of the
  11 file type requires heavy-duty semantic analysis on the file contents.
  12 It is, however, possible to obtain satisfactory results by employing
  13 various heuristics.
  14 
  15 Previous versions of PKZip and other zip-compatible compression tools
  16 were using a crude detection scheme: if more than 80% (4/5) of the bytes
  17 found in a certain buffer are within the range [7..127], the file is
  18 labeled as plain text, otherwise it is labeled as binary.  A prominent
  19 limitation of this scheme is the restriction to Latin-based alphabets.
  20 Other alphabets, like Greek, Cyrillic or Asian, make extensive use of
  21 the bytes within the range [128..255], and texts using these alphabets
  22 are most often misidentified by this scheme; in other words, the rate
  23 of false negatives is sometimes too high, which means that the recall
  24 is low.  Another weakness of this scheme is a reduced precision, due to
  25 the false positives that may occur when binary files containing large
  26 amounts of textual characters are misidentified as plain text.
  27 
  28 In this article we propose a new, simple detection scheme that features
  29 a much increased precision and a near-100% recall.  This scheme is
  30 designed to work on ASCII, Unicode and other ASCII-derived alphabets,
  31 and it handles single-byte encodings (ISO-8859, MacRoman, KOI8, etc.)
  32 and 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 
  36 The Algorithm
  37 -------------
  38 
  39 The algorithm works by dividing the set of bytecodes [0..255] into three
  40 categories:
  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 
  48 If a file contains at least one byte that belongs to the white list and
  49 no byte that belongs to the black list, then the file is categorized as
  50 plain text; otherwise, it is categorized as binary.  (The boundary case,
  51 when the file is empty, automatically falls into the latter category.)
  52 
  53 
  54 Rationale
  55 ---------
  56 
  57 The idea behind this algorithm relies on two observations.
  58 
  59 The first observation is that, although the full range of 7-bit codes
  60 [0..127] is properly specified by the ASCII standard, most control
  61 characters in the range [0..31] are not used in practice.  The only
  62 widely-used, almost universally-portable control codes are 9 (TAB),
  63 10 (LF) and 13 (CR).  There are a few more control codes that are
  64 recognized on a reduced range of platforms and text viewers/editors:
  65 7 (BEL), 8 (BS), 11 (VT), 12 (FF), 26 (SUB) and 27 (ESC); but these
  66 codes are rarely (if ever) used alone, without being accompanied by
  67 some printable text.  Even the newer, portable text formats such as
  68 XML avoid using control characters outside the list mentioned here.
  69 
  70 The second observation is that most of the binary files tend to contain
  71 control characters, especially 0 (NUL).  Even though the older text
  72 detection 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
  74 labeled as textual, because the files that are genuinely binary tend to
  75 contain both control characters and codes from the upper range.  On the
  76 other hand, the upper range needs to be labeled as textual, because it
  77 is used by virtually all ASCII extensions.  In particular, this range is
  78 used for encoding non-Latin scripts.
  79 
  80 Since there is no counting involved, other than simply observing the
  81 presence or the absence of some byte values, the algorithm produces
  82 consistent results, regardless what alphabet encoding is being used.
  83 (If counting were involved, it could be possible to obtain different
  84 results on a text encoded, say, using ISO-8859-16 versus UTF-8.)
  85 
  86 There is an extra category of plain text files that are "polluted" with
  87 one or more black-listed codes, either by mistake or by peculiar design
  88 considerations.  In such cases, a scheme that tolerates a small fraction
  89 of black-listed codes would provide an increased recall (i.e. more true
  90 positives).  This, however, incurs a reduced precision overall, since
  91 false positives are more likely to appear in binary files that contain
  92 large chunks of textual data.  Furthermore, "polluted" plain text should
  93 be regarded as binary by general-purpose text detection schemes, because
  94 general-purpose text processing algorithms might not be applicable.
  95 Under this premise, it is safe to say that our detection method provides
  96 a near-100% recall.
  97 
  98 Experiments have been run on many files coming from various platforms
  99 and applications.  We tried plain text files, system logs, source code,
 100 formatted office documents, compiled object code, etc.  The results
 101 confirm the optimistic assumptions about the capabilities of this
 102 algorithm.
 103 
 104 
 105 --
 106 Cosmin Truta
 107 Last updated: 2006-May-28