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dependencies/zlib-1.2.11/doc/txtvsbin.txt

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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