diff options
author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-05-04 18:00:34 +0000 |
---|---|---|
committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-05-04 18:00:34 +0000 |
commit | 3f619478f796eddbba6e39502fe941b285dd97b1 (patch) | |
tree | e2c7b5777f728320e5b5542b6213fd3591ba51e2 /libmariadb/external/zlib/doc/txtvsbin.txt | |
parent | Initial commit. (diff) | |
download | mariadb-3f619478f796eddbba6e39502fe941b285dd97b1.tar.xz mariadb-3f619478f796eddbba6e39502fe941b285dd97b1.zip |
Adding upstream version 1:10.11.6.upstream/1%10.11.6upstream
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'libmariadb/external/zlib/doc/txtvsbin.txt')
-rw-r--r-- | libmariadb/external/zlib/doc/txtvsbin.txt | 107 |
1 files changed, 107 insertions, 0 deletions
diff --git a/libmariadb/external/zlib/doc/txtvsbin.txt b/libmariadb/external/zlib/doc/txtvsbin.txt new file mode 100644 index 00000000..3d0f0634 --- /dev/null +++ b/libmariadb/external/zlib/doc/txtvsbin.txt @@ -0,0 +1,107 @@ +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 |