1 A Fast Method for Identifying Plain Text Files 2 ============================================== 3 4 5 Introduction 6 ------------ 7 8 Given a file coming from an unknown source, it is sometimes desirable 9 to find out whether the format of that file is plain text. Although 10 this may appear like a simple task, a fully accurate detection of the 11 file type requires heavy-duty semantic analysis on the file contents. 12 It is, however, possible to obtain satisfactory results by employing 13 various heuristics. 14 15 Previous versions of PKZip and other zip-compatible compression tools 16 were using a crude detection scheme: if more than 80% (4/5) of the bytes 17 found in a certain buffer are within the range [7..127], the file is 18 labeled as plain text, otherwise it is labeled as binary. A prominent 19 limitation of this scheme is the restriction to Latin-based alphabets. 20 Other alphabets, like Greek, Cyrillic or Asian, make extensive use of 21 the bytes within the range [128..255], and texts using these alphabets 22 are most often misidentified by this scheme; in other words, the rate 23 of false negatives is sometimes too high, which means that the recall 24 is low. Another weakness of this scheme is a reduced precision, due to 25 the false positives that may occur when binary files containing large 26 amounts of textual characters are misidentified as plain text. 27 28 In this article we propose a new, simple detection scheme that features 29 a much increased precision and a near-100% recall. This scheme is 30 designed to work on ASCII, Unicode and other ASCII-derived alphabets, 31 and it handles single-byte encodings (ISO-8859, MacRoman, KOI8, etc.) 32 and variable-sized encodings (ISO-2022, UTF-8, etc.). Wider encodings 33 (UCS-2/UTF-16 and UCS-4/UTF-32) are not handled, however. 34 35 36 The Algorithm 37 ------------- 38 39 The algorithm works by dividing the set of bytecodes [0..255] into three 40 categories: 41 - The white list of textual bytecodes: 42 9 (TAB), 10 (LF), 13 (CR), 32 (SPACE) to 255. 43 - The gray list of tolerated bytecodes: 44 7 (BEL), 8 (BS), 11 (VT), 12 (FF), 26 (SUB), 27 (ESC). 45 - The black list of undesired, non-textual bytecodes: 46 0 (NUL) to 6, 14 to 31. 47 48 If a file contains at least one byte that belongs to the white list and 49 no byte that belongs to the black list, then the file is categorized as 50 plain text; otherwise, it is categorized as binary. (The boundary case, 51 when the file is empty, automatically falls into the latter category.) 52 53 54 Rationale 55 --------- 56 57 The idea behind this algorithm relies on two observations. 58 59 The first observation is that, although the full range of 7-bit codes 60 [0..127] is properly specified by the ASCII standard, most control 61 characters in the range [0..31] are not used in practice. The only 62 widely-used, almost universally-portable control codes are 9 (TAB), 63 10 (LF) and 13 (CR). There are a few more control codes that are 64 recognized on a reduced range of platforms and text viewers/editors: 65 7 (BEL), 8 (BS), 11 (VT), 12 (FF), 26 (SUB) and 27 (ESC); but these 66 codes are rarely (if ever) used alone, without being accompanied by 67 some printable text. Even the newer, portable text formats such as 68 XML avoid using control characters outside the list mentioned here. 69 70 The second observation is that most of the binary files tend to contain 71 control characters, especially 0 (NUL). Even though the older text 72 detection schemes observe the presence of non-ASCII codes from the range 73 [128..255], the precision rarely has to suffer if this upper range is 74 labeled as textual, because the files that are genuinely binary tend to 75 contain both control characters and codes from the upper range. On the 76 other hand, the upper range needs to be labeled as textual, because it 77 is used by virtually all ASCII extensions. In particular, this range is 78 used for encoding non-Latin scripts. 79 80 Since there is no counting involved, other than simply observing the 81 presence or the absence of some byte values, the algorithm produces 82 consistent results, regardless what alphabet encoding is being used. 83 (If counting were involved, it could be possible to obtain different 84 results on a text encoded, say, using ISO-8859-16 versus UTF-8.) 85 86 There is an extra category of plain text files that are "polluted" with 87 one or more black-listed codes, either by mistake or by peculiar design 88 considerations. In such cases, a scheme that tolerates a small fraction 89 of black-listed codes would provide an increased recall (i.e. more true 90 positives). This, however, incurs a reduced precision overall, since 91 false positives are more likely to appear in binary files that contain 92 large chunks of textual data. Furthermore, "polluted" plain text should 93 be regarded as binary by general-purpose text detection schemes, because 94 general-purpose text processing algorithms might not be applicable. 95 Under this premise, it is safe to say that our detection method provides 96 a near-100% recall. 97 98 Experiments have been run on many files coming from various platforms 99 and applications. We tried plain text files, system logs, source code, 100 formatted office documents, compiled object code, etc. The results 101 confirm the optimistic assumptions about the capabilities of this 102 algorithm. 103 104 105 -- 106 Cosmin Truta 107 Last updated: 2006-May-28 108
