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-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 white 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 black 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 white list and
-no byte that belongs to the black 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 black-listed codes, either by mistake or by peculiar design
-considerations.  In such cases, a scheme that tolerates a small fraction
-of black-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