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diff --git a/cpukit/compression/zlib/doc/algorithm.txt b/cpukit/compression/zlib/doc/algorithm.txt
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+1. Compression algorithm (deflate)
+
+The deflation algorithm used by gzip (also zip and zlib) is a variation of
+LZ77 (Lempel-Ziv 1977, see reference below). It finds duplicated strings in
+the input data.  The second occurrence of a string is replaced by a
+pointer to the previous string, in the form of a pair (distance,
+length).  Distances are limited to 32K bytes, and lengths are limited
+to 258 bytes. When a string does not occur anywhere in the previous
+32K bytes, it is emitted as a sequence of literal bytes.  (In this
+description, `string' must be taken as an arbitrary sequence of bytes,
+and is not restricted to printable characters.)
+
+Literals or match lengths are compressed with one Huffman tree, and
+match distances are compressed with another tree. The trees are stored
+in a compact form at the start of each block. The blocks can have any
+size (except that the compressed data for one block must fit in
+available memory). A block is terminated when deflate() determines that
+it would be useful to start another block with fresh trees. (This is
+somewhat similar to the behavior of LZW-based _compress_.)
+
+Duplicated strings are found using a hash table. All input strings of
+length 3 are inserted in the hash table. A hash index is computed for
+the next 3 bytes. If the hash chain for this index is not empty, all
+strings in the chain are compared with the current input string, and
+the longest match is selected.
+
+The hash chains are searched starting with the most recent strings, to
+favor small distances and thus take advantage of the Huffman encoding.
+The hash chains are singly linked. There are no deletions from the
+hash chains, the algorithm simply discards matches that are too old.
+
+To avoid a worst-case situation, very long hash chains are arbitrarily
+truncated at a certain length, determined by a runtime option (level
+parameter of deflateInit). So deflate() does not always find the longest
+possible match but generally finds a match which is long enough.
+
+deflate() also defers the selection of matches with a lazy evaluation
+mechanism. After a match of length N has been found, deflate() searches for
+a longer match at the next input byte. If a longer match is found, the
+previous match is truncated to a length of one (thus producing a single
+literal byte) and the process of lazy evaluation begins again. Otherwise,
+the original match is kept, and the next match search is attempted only N
+steps later.
+
+The lazy match evaluation is also subject to a runtime parameter. If
+the current match is long enough, deflate() reduces the search for a longer
+match, thus speeding up the whole process. If compression ratio is more
+important than speed, deflate() attempts a complete second search even if
+the first match is already long enough.
+
+The lazy match evaluation is not performed for the fastest compression
+modes (level parameter 1 to 3). For these fast modes, new strings
+are inserted in the hash table only when no match was found, or
+when the match is not too long. This degrades the compression ratio
+but saves time since there are both fewer insertions and fewer searches.
+
+
+2. Decompression algorithm (inflate)
+
+2.1 Introduction
+
+The key question is how to represent a Huffman code (or any prefix code) so
+that you can decode fast.  The most important characteristic is that shorter
+codes are much more common than longer codes, so pay attention to decoding the
+short codes fast, and let the long codes take longer to decode.
+
+inflate() sets up a first level table that covers some number of bits of
+input less than the length of longest code.  It gets that many bits from the
+stream, and looks it up in the table.  The table will tell if the next
+code is that many bits or less and how many, and if it is, it will tell
+the value, else it will point to the next level table for which inflate()
+grabs more bits and tries to decode a longer code.
+
+How many bits to make the first lookup is a tradeoff between the time it
+takes to decode and the time it takes to build the table.  If building the
+table took no time (and if you had infinite memory), then there would only
+be a first level table to cover all the way to the longest code.  However,
+building the table ends up taking a lot longer for more bits since short
+codes are replicated many times in such a table.  What inflate() does is
+simply to make the number of bits in the first table a variable, and  then
+to set that variable for the maximum speed.
+
+For inflate, which has 286 possible codes for the literal/length tree, the size
+of the first table is nine bits.  Also the distance trees have 30 possible
+values, and the size of the first table is six bits.  Note that for each of
+those cases, the table ended up one bit longer than the ``average'' code
+length, i.e. the code length of an approximately flat code which would be a
+little more than eight bits for 286 symbols and a little less than five bits
+for 30 symbols.
+
+
+2.2 More details on the inflate table lookup
+
+Ok, you want to know what this cleverly obfuscated inflate tree actually
+looks like.  You are correct that it's not a Huffman tree.  It is simply a
+lookup table for the first, let's say, nine bits of a Huffman symbol.  The
+symbol could be as short as one bit or as long as 15 bits.  If a particular
+symbol is shorter than nine bits, then that symbol's translation is duplicated
+in all those entries that start with that symbol's bits.  For example, if the
+symbol is four bits, then it's duplicated 32 times in a nine-bit table.  If a
+symbol is nine bits long, it appears in the table once.
+
+If the symbol is longer than nine bits, then that entry in the table points
+to another similar table for the remaining bits.  Again, there are duplicated
+entries as needed.  The idea is that most of the time the symbol will be short
+and there will only be one table look up.  (That's whole idea behind data
+compression in the first place.)  For the less frequent long symbols, there
+will be two lookups.  If you had a compression method with really long
+symbols, you could have as many levels of lookups as is efficient.  For
+inflate, two is enough.
+
+So a table entry either points to another table (in which case nine bits in
+the above example are gobbled), or it contains the translation for the symbol
+and the number of bits to gobble.  Then you start again with the next
+ungobbled bit.
+
+You may wonder: why not just have one lookup table for how ever many bits the
+longest symbol is?  The reason is that if you do that, you end up spending
+more time filling in duplicate symbol entries than you do actually decoding.
+At least for deflate's output that generates new trees every several 10's of
+kbytes.  You can imagine that filling in a 2^15 entry table for a 15-bit code
+would take too long if you're only decoding several thousand symbols.  At the
+other extreme, you could make a new table for every bit in the code.  In fact,
+that's essentially a Huffman tree.  But then you spend too much time
+traversing the tree while decoding, even for short symbols.
+
+So the number of bits for the first lookup table is a trade of the time to
+fill out the table vs. the time spent looking at the second level and above of
+the table.
+
+Here is an example, scaled down:
+
+The code being decoded, with 10 symbols, from 1 to 6 bits long:
+
+A: 0
+B: 10
+C: 1100
+D: 11010
+E: 11011
+F: 11100
+G: 11101
+H: 11110
+I: 111110
+J: 111111
+
+Let's make the first table three bits long (eight entries):
+
+000: A,1
+001: A,1
+010: A,1
+011: A,1
+100: B,2
+101: B,2
+110: -> table X (gobble 3 bits)
+111: -> table Y (gobble 3 bits)
+
+Each entry is what the bits decode as and how many bits that is, i.e. how
+many bits to gobble.  Or the entry points to another table, with the number of
+bits to gobble implicit in the size of the table.
+
+Table X is two bits long since the longest code starting with 110 is five bits
+long:
+
+00: C,1
+01: C,1
+10: D,2
+11: E,2
+
+Table Y is three bits long since the longest code starting with 111 is six
+bits long:
+
+000: F,2
+001: F,2
+010: G,2
+011: G,2
+100: H,2
+101: H,2
+110: I,3
+111: J,3
+
+So what we have here are three tables with a total of 20 entries that had to
+be constructed.  That's compared to 64 entries for a single table.  Or
+compared to 16 entries for a Huffman tree (six two entry tables and one four
+entry table).  Assuming that the code ideally represents the probability of
+the symbols, it takes on the average 1.25 lookups per symbol.  That's compared
+to one lookup for the single table, or 1.66 lookups per symbol for the
+Huffman tree.
+
+There, I think that gives you a picture of what's going on.  For inflate, the
+meaning of a particular symbol is often more than just a letter.  It can be a
+byte (a "literal"), or it can be either a length or a distance which
+indicates a base value and a number of bits to fetch after the code that is
+added to the base value.  Or it might be the special end-of-block code.  The
+data structures created in inftrees.c try to encode all that information
+compactly in the tables.
+
+
+Jean-loup Gailly        Mark Adler
+jloup@gzip.org          madler@alumni.caltech.edu
+
+
+References:
+
+[LZ77] Ziv J., Lempel A., ``A Universal Algorithm for Sequential Data
+Compression,'' IEEE Transactions on Information Theory, Vol. 23, No. 3,
+pp. 337-343.
+
+``DEFLATE Compressed Data Format Specification'' available in
+http://tools.ietf.org/html/rfc1951
diff --git a/cpukit/zlib/doc/rfc1950.txt b/cpukit/compression/zlib/doc/rfc1950.txt
index ce6428a0f2..ce6428a0f2 100644
--- a/cpukit/zlib/doc/rfc1950.txt
+++ b/cpukit/compression/zlib/doc/rfc1950.txt
diff --git a/cpukit/zlib/doc/rfc1951.txt b/cpukit/compression/zlib/doc/rfc1951.txt
index 403c8c722f..403c8c722f 100644
--- a/cpukit/zlib/doc/rfc1951.txt
+++ b/cpukit/compression/zlib/doc/rfc1951.txt
diff --git a/cpukit/zlib/doc/rfc1952.txt b/cpukit/compression/zlib/doc/rfc1952.txt
index a8e51b4567..a8e51b4567 100644
--- a/cpukit/zlib/doc/rfc1952.txt
+++ b/cpukit/compression/zlib/doc/rfc1952.txt
diff --git a/cpukit/compression/zlib/doc/txtvsbin.txt b/cpukit/compression/zlib/doc/txtvsbin.txt
new file mode 100644
index 0000000000..2a901eaa68
--- /dev/null
+++ b/cpukit/compression/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 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