FreeBSD/Linux Kernel Cross Reference
sys/contrib/zlib/doc/txtvsbin.txt

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

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