Microsoft Word - likforman_et_al_sept06.doc
submitted to Special Issue on Analysis of Historical Documents, International Journal 
on Document Analysis and Recognition, Springer, 2006. 
Text Line Segmentation of Historical Documents: a 
Survey 
Laurence Likforman-Sulem*, Abderrazak Zahour**, Bruno Taconet** 
*GET-Ecole Nationale Supérieure des Télécommunications/TSI and CNRS-LTCI, 46  rue 
Barrault, 75013 Paris, France 
email: likforman@tsi.enst.fr 
Phone: +33 1 45 81 73 28 
Fax: +33 1 45 81 37 94 
http://www.tsi.enst.fr/~lauli/ 
** IUT, Université du Havre/GED, Place Robert Schuman, 76610 Le Havre, France 
email :{taconet|zahour}@univ-lehavre.fr 
Abstract 
There is a huge amount of historical documents in libraries and in various National Archives that have not been 
exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term 
objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are 
in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low 
quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines), 
automatic text line segmentation remains an open research field. The objective of this paper is to present a 
survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.  
Keywords: segmentation, handwriting, text lines, Historical documents, survey 
1. Introduction  
Text line extraction is generally seen as a preprocessing step for tasks such as document 
structure extraction, printed character or handwriting recognition. Many techniques have been 
developed for page segmentation of printed documents (newspapers, scientific journals, 
magazines, business letters) produced with modern editing tools [57] [38] [14] [39] [2]. The 
segmentation of handwritten documents has also been addressed with the segmentation of 
address blocks on envelopes and mail pieces [9] [10] [15][48], and for authentication or 
recognition purposes [53] [60]. More recently, the development of handwritten text databases 
(IAM database, [34]) provides new material for handwritten page segmentation.  
Ancient and historical documents, printed or handwritten, strongly differ from the documents 
mentioned above because layout formatting requirements were looser. Their physical 
structure is thus harder to extract. In addition, historical documents are of low quality, due to 
aging or faint typing. They include various disturbing elements such as holes, spots, writing 
from the verso appearing on the recto, ornamentation, or seals. Handwritten pages include 
narrow spaced lines with overlapping and touching components. Characters and words have 
unusual and varying shapes, depending on the writer, the period and the place concerned. The 
vocabulary is also large and may include unusual names and words. Full text recognition is in 
most cases not yet available, except for printed documents for which dedicated OCR can be 
developed.  
However, invaluable collections of historical documents are already digitized and indexed for 
consulting, exchange and distant access purposes which protect them from direct 
manipulation. In some cases, highly structured editions have been established by scholars. But 
a huge amount of documents are still to be exploited electronically. To produce an electronic 
searchable form, a document has to be indexed. The simplest way of indexing a document 
consists in attaching its main characteristics such as date, place and author (the so called 
‘metadata’). Indexing can be enhanced when the document structure and content are 
exploited. When a transcription (published version, diplomatic transcription) is available, it 
can be attached to the digitized document: this allows users to retrieve documents from 
textual queries.  
Since text based representations do not reflect the graphical features of such documents, a 
better representation is obtained by linking the transcription to the document image. A direct 
correspondence can then be established between the document image and its content by 
text/image alignment techniques [55]. This allows the creation of indexes where the position 
of each word can be recorded, and of links between both representations. Clicking on a word 
on the transcription or in the index through a GUI allows users to visualize the corresponding 
image area and vice versa. To make such queries possible for handwritten sources of literary 
works, several projects have been carried out under EU and National Programs: for instance 
the so-called ‘philological workstation’ Bambi [6][8] and within the Philectre reading and 
editing environment [47]. The document analysis embedded in such systems provides tools to 
search for blocks, lines and words, and may include a dedicated handwriting recognition 
system. Interactive tools are generally offered for segmentation and recognition correction 
purposes. Several projects also concern printed material: Debora [5] and Memorial [3]. Partial 
or complete logical structure can also be extracted by document analysis and corrected with 
GUI as in the Viadocs project [11][18]. However, document structure can also be used when 
no transcription is available. Word spotting techniques [22] [55] [46] can retrieve similar 
words in the image document through an image query. When words of the image document 
are extracted by top down segmentation, which is generally the case, text lines are extracted 
first.  
                                                                 
Fig. 1.  Examples of historical documents a) Provencal medieval manuscript.  b) one page from De Gaulle’s 
diaries c) an ancient Arabic document from Tunisian Archives. 
The authentication of manuscripts in the paleographic sense can also make use of document 
analysis and text line extraction. Authentication consists in retrieving writer characteristics 
independently from document content. It generally consists in dating documents, localizing 
the place where the document was produced, identifying the writer by using characteristics 
and features extracted from blank spaces, line orientations and fluctuations, word or character 
shapes [43] [27] [4]. 
Page segmentation into text lines is performed in most tasks mentioned above and overall 
performance strongly relies on the quality of this process. The purpose of this article is to 
survey the efforts made for historical documents on the text line segmentation task. Section 2 
describes the characteristics of text line structures in historical documents and the different 
ways of defining a text line. Preprocessing of document images (gray level, color or black and 
white) is often necessary before text line extracting to prune superfluous information (non 
textual elements, textual elements from the verso) or to correctly binarize the image.  This 
problem is addressed in Section 3.1. In Sections 3.2-3.7 we survey the different approaches to 
segment the clean image into text lines. A taxonomy is proposed, listed as projection profiles, 
smearing, grouping, Hough-based, repulsive-attractive network and stochastic methods. The 
majority of these techniques have been developed for the projects on historical documents 
mentioned above. In Section 3.8, we address the specific problem of overlapping and 
touching components. Concluding remarks are given in Section 4.  
Fig. 2.  Reference lines and interfering lines with overlapping and touching components. 
2. Characteristics and representation of text lines  
To have a good idea of the physical structure of a document image, one only needs to look at 
it from a certain distance: the lines and the blocks are immediately visible. These blocks 
consist of columns, annotations in margins, stanzas, etc... As blocks generally have no 
rectangular shape in historical documents, the text line structure becomes the dominant 
physical structure. We first give some definitions about text line components and text line 
segmentation. Then we describe the factors which make this text line segmentation hard. 
Finally, we describe how a text line can be represented.  
2.1 Definitions 
baseline: fictitious line which follows and joins the lower part of the character bodies in a text 
line (Fig. 2) 
median line: fictitious line which follows and joins the upper part of the character bodies in a 
text line. 
upper line: fictitious line which joins the top of ascenders. 
lower line: fictitious line which joins the bottom of descenders. 
overlapping components: overlapping components are descenders and ascenders located in 
the region of an adjacent line (Fig. 2).   
touching components: touching components are ascenders and descenders belonging to 
consecutive lines which are thus connected. These components are large but hard to 
discriminate before text lines are known.  
text line segmentation: text line segmentation is a labeling process which consists in assigning 
the same label to spatially aligned units (such as pixels, connected components or 
characteristic points). There are two categories of text line segmentation approaches: 
searching for (fictitious) separating lines or paths, or searching for aligned physical units. The 
choice of a segmentation technique depends on the complexity of the text line structure of the 
document. 
2.2 Influence of author style 
baseline fluctuation: the baseline may vary due to writer movement. It may be straight, 
straight by segments, or curved.  
line orientations: there may be different line orientations, especially on authorial works where 
there are corrections and annotations.   
line spacing: lines that are rather widely spaced lines are easy to find. The process of 
extracting text lines grows more difficult as interlines are narrowing; the lower baseline of the 
first line is becoming closer to the upper baseline of the second line; also, descenders and 
ascenders start to fill the blank space left for separating two adjacent text lines (Fig. 3).  
insertions: words or short text lines may appear between the principal text lines, or in the 
margins.  
2.3 Influence of poor image quality 
imperfect preprocessing: smudges, variable background intensity and the presence of seeping 
ink from the other side of the document make image preprocessing particularly difficult and 
produce binarization errors.  
stroke fragmentation and merging: punctuation, dots and broken strokes due to low-quality 
images and/or binarization may produce many connected components; conversely, words, 
characters and strokes may be split into several connected components. The broken 
components are no longer linked to the median baseline of the writing and become ambiguous 
and hard to segment into the correct text line (Fig. 3). 
2.4 Text line representation 
separating paths and delimited strip: separating lines (or paths) are continuous fictitious lines 
which can be uniformly straight, made of straight segments, or of curving joined strokes. The 
delimited strip between two consecutive separating lines receives the same text line label. So 
the text line can be represented by a strip with its couple of separating lines (Fig. 4). 
clusters: clusters are a general set-based way of defining text lines. A label is associated with 
each cluster. Units within the same cluster belong to the same text line. They may be pixels, 
connected components, or blocks enclosing pieces of writing. A text line can be represented 
by a list of units with the same label. 
Fig. 3.  The three main axes of document complexity for text line segmentation. 
Fig. 4. Various text line representations: paths, strings and baselines. 
strings: strings are lists of spatially aligned and ordered units. Each  string represents one text 
line.  
baselines: baselines follow line fluctuations but partially define a text line. Units connected to 
a baseline are assumed to belong to it. Complementary processing has to be done to cluster 
non-connected units and touching components. 
line    proximity
line fluctuation
writing fragmentation
line    proximity
line fluctuation
writing fragmentation
3. Text line segmentation  
Printed historical documents belong to a large period from 16th to 20th centuries (reports, 
ancient books, registers, card archives). Their printing may be faint, producing writing 
fragmentation artifacts. However, text lines are still enclosed in rectangular areas. After the 
text part has been extracted and restored, top-down and smearing techniques are generally 
applied for text line segmentation. A large proportion of historical documents are handwritten: 
scrolls, registers, private and official letters, authorial drafts. The type of writing differs 
considerably from one document to another. It can be calligraphed or cursive; various styles 
can be observed (Fig. 1). In the context of cursive handwriting, statistical information about 
line spacing and line orientation is hard to capture. Several techniques, which take into 
account handwriting and layout irregularities, as well as local and global characteristics of the 
text lines, have been developed 
3.1 Preprocessing 
Text line extraction would ideally process document images without background noise and 
without non-textual elements; the writing would be well contrasted with as little 
fragmentation as possible. In reality, preprocessing is often necessary. Although 
preprocessing has to be accurately adapted to each document and to its characteristics, we 
shortly describe here some preprocessing techniques that can be performed before text line 
extraction.  
Non-textual elements around the text such as book bindings, book sides, parts of fingers 
(thumb marks from someone holding the book open f.i.) should be removed upon criteria such 
as position and intensity level. On the document itself, holes, stains, may be removed by high-
pass filtering [12]. Other non-textual elements (stamps, seals) but also ornamentation, 
decorated initials, can be removed using knowledge about the shape, the color or the position 
of these elements [17]. Extracting text from figures (text segmentation) can also be performed 
on texture grounds [20][36] or by morphological filters [16][37]. Linear graphical elements 
such as big crosses (called “St Andre’s crosses”) appear in some of Flaubert’s manuscripts. 
Removing these elements is performed through GUI by Kalman filtering in [31].   
Textual but unwanted elements such as the writing on the verso (bleed through text) may be 
removed by filtering and wavelet techniques [24][54][32] and by combining the verso image 
(the reverse side image) with the recto one (front side image).  
Binarization, if necessary, can be performed by global or local thresholding. Global 
thresholding algorithms are not generally applicable to historical documents, due to 
inhomogeneous background. Thus, global thresholding results in severe deterioration in the 
quality of the segmented document image. Several local thresholding techniques have already 
been proposed to partially overcome such difficulties [21]. These local methods determine the 
threshold values based on the local properties of an image, e.g. pixel-by-pixel or region-by-
region, and yield relatively better binarization results when compared with global 
thresholding methods. Writing may be faint so that over-segmentation or under-segmentation 
may occur. The integral ratio technique [52] is a two-stage segmentation technique adapted to 
this problem. Background normalization [51] can be performed before binarization in order to 
find a global threshold more easily.  
3.2 Projection–based methods  
Projection-profiles are commonly used for printed document segmentation. This technique 
can also be adapted to handwritten documents with little overlap. The vertical projection-
profile is obtained by summing pixel values along the horizontal axis for each y value. From 
the vertical profile, the gaps between the text lines in the vertical direction can be observed 
(Fig. 5). 
yxfyprofile
),()(   
The vertical profile is not sensitive to writing fragmentation. Variants for obtaining a profile 
curve may consist in projecting black/white transitions such as in Marti and Bunke [35] or the 
number of connected components, rather than pixels. The profile curve can be smoothed, e.g. 
by a Gaussian or median filter to eliminate local maxima [33]. The profile curve is then 
analysed to find its maxima and minima. There are two drawbacks: short lines will provide 
low peaks, and very narrow lines, as well as those including many overlapping components 
will not produce significant peaks. In case of skew or moderate fluctuations of the text lines, 
the image may be divided into vertical strips and profiles sought inside each strip (Zahour et 
al. [58]). These piecewise projections are thus a means of adapting to local fluctuations within 
a more global scheme.  
In Shapiro et al.[49], the global orientation (skew angle) of a handwritten page is first 
searched by applying a Hough transform on the entire image. Once this skew angle is 
obtained, projections are achieved along this angle. The number of maxima of the profile give 
the number of lines. Low maxima are discarded on their value, which is compared to the 
highest maxima. Lines are delimited by strips, searching for the minima of projection profiles 
around each maxima. This technique has been tested on a set of 200 pages within a word 
segmentation task.  
In the work of Antonacopoulos and Karatzas [3], each minimum of the profile curve is a 
potential segmentation point.  Potential points are then scored according to their distance to 
adjacent segmentation points. The reference distance is obtained from the histogram of 
distances between adjacent potential segmentation points. The highest scored segmentation 
point is used as an anchor to derive the remaining ones. The method is applied to printed 
records of the second World War which have regularly spaced text lines. The logical structure 
is used to derive the text regions where the names of interest can be found. 
Fig. 5.  Vertical projection-profile extracted on an autograph of Jean-Paul Sartre. 
The RXY cuts method applied in He and Downton [18], uses alternating projections along the 
X and the Y axis. This results in a hierarchical tree structure. Cuts are found within white 
spaces. Thresholds are necessary to derive inter-line or inter-block distances. This method can 
be applied to printed documents (which are assumed to have these regular distances) or well 
separated handwritten lines. 
3.3 Smearing methods 
For printed and binarized documents, smearing methods such as the Run-Length Smoothing 
Algorithm (Wong et al. [57]) can be applied. Consecutive black pixels along the horizontal 
direction are smeared: i.e. the white space between them is filled with black pixels if their 
distance is within a predefined threshold.  The bounding boxes of the connected components 
in the smeared image enclose text lines.  
A variant of this method adapted to gray level images and applied to printed books from the 
sixteenth century consists in accumulating the image gradient along the horizontal direction 
(LeBourgeois [25]). This method has been adapted to old printed documents within the 
Debora project [26]. For this purpose, numerous adjustments in the method concern the 
tolerance for character alignment and line justification.  
Text line patterns are found in the work of Shi and Govindaraju [50] by building a fuzzy run 
length matrix. At each pixel, the fuzzy run-length is the maximal extent of the background 
along the horizontal direction. Some foreground pixels may be skipped if their number does 
not exceed a predefined value. This matrix is threshold to make pieces of text lines appear 
without ascenders and descenders (Fig. 6).  Parameters have to be accurately and dynamically 
tuned.  
3.4 Grouping methods  
These methods consist in building alignments by aggregating units in a bottom-up strategy. 
The units may be pixels or of higher level, such as connected components, blocks or other 
features such as salient points. Units are then joined together to form alignments. The joining 
scheme relies on both local and global criteria, which are used for checking local and global 
consistency respectively. 
 10  
Fig. 6 Text line patterns extracted from a letter of Georges Washington (reprinted from Shi and Govindaraju 
[50], © [2004] IEEE). Foreground pixels have been smeared along the horizontal direction. 
Contrary to printed documents, a simple nearest-neighbor joining scheme would often fail to 
group complex handwritten units, as the nearest neighbor often belongs to another line. The 
joining criteria used in the methods described below are adapted to the type of the units and 
the characteristics of the documents under study. But every method has to face the following: 
- initiating alignments: one or several seeds for each alignment. 
- defining a unit’s neighborhood for reaching the next unit. It is generally a rectangular or 
angular area (Fig. 7).  
- solving conflicts. As one unit may belong to several alignments under construction, a choice 
has to be made: discard one alignment or keep both of them, cutting the unit into several parts. 
Hence, these methods include one or several quality measures which ensure that the text line 
under construction is of good quality. When comparing the quality measures of two 
alignments in conflict, the alignment of lower quality can be discarded (Fig. 7).  Also, during 
the grouping process, it is possible to choose between the different units that can be 
aggregated within the same neighborhood by evaluating the quality of each of the so-formed 
alignments.  
 11  
Fig. 7. Angular and rectangular neighborhoods from point and rectangular units (left). Neighborhood defined by 
a cluster of units (upright).  Two alignments A and B in conflict: a quality measure will choose A and discard B 
(down right). 
Quality measures generally include the strength of the alignment, i.e. the number of units 
included. Other quality elements may concern component size, component spacing, or a 
measure of the alignment’s straightness.  
Fig. 8. Text lines extracted on Church Registers (reprinted from Feldbach [12] with permission from the author). 
Likforman-Sulem and Faure have developed in [28] an iterative method based on perceptual 
grouping for forming alignments, which has been applied to handwritten pages, author drafts 
and historical documents [29][47]. Anchors are detected by selecting connected components 
elongated in specific directions (0°, 45°, 90°, 125°). Each of these anchors becomes the seed 
of an alignment. First, each anchor, then each alignment, is extended to the left and to the 
right. This extension uses three Gestalt criteria for grouping components: proximity, similarity 
and direction continuity. The threshold is iteratively incremented in order to group 
components within a broader neighborhood until no change occurs. Between each iteration, 
alignment quality is checked by a quality measure which gives higher rates to long alignments 
including anchors of the same direction. A penalty is given when the alignment includes 
anchors of different directions. Two alignments may cross each other, or overlap. A set of 
 12  
rules is applied to solve these conflicts taking into account the quality of each alignment and 
neighboring components of higher order (Fig. 14). 
In the work of Feldbach and Tönnies [12][13], body baselines are searched in Church 
Registers images. These documents include lots of fluctuating and overlapping lines. 
Baselines units are the minima points of the writing  (obtained here from the skeleton). First 
basic line segments (BLS) are constructed, joining each minima point to its neighbors.  This 
neighborhood is defined by an angular region (+-20°) for the first unit grouped, then by a 
rectangular region enclosing the points already joined for the remaining ones. Unwanted basic 
segments are found from minima points detected in descenders and ascenders. These 
segments may be isolated or in conflict with others. Various heuristics are defined to 
eliminate alignments on their size, or the local inter-line distance and on a quality measure 
which favours alignments whose units are in the same direction rather than nearer units but 
positioned lower or higher than the current direction. Conflicting alignments can be 
reconstructed depending on the topology of the conflicting alignments. The median line is 
searched from the baseline and from maxima points (Fig. 8). Pixels lying within a given 
baseline and median line are clustered in the corresponding text line, while ascenders and 
descenders are not segmented. Correct segmentation rates are reported between 90% and 97 
% with adequate parameter adjustment. The seven documents tested range from the 17th to the 
19th century. 
3.5 Methods based on the Hough transform 
The Hough transform is a very popular technique [19] for finding straight lines in images. In 
Likforman-Sulem et al. [30], a method has been developed on a hypothesis-validation 
scheme. Potential alignments are hypothesized in the Hough domain and validated in the 
Image domain. Thus, no assumption is made about text line directions (several may exist 
within the same page). The centroids of the connected components are the units for the Hough 
transform. A set of aligned units in the image along a line with parameters (ρ, θ) is included 
in the corresponding cell (ρ, θ) of the Hough domain. Alignments including a lot of units 
correspond to high peaked cells of the Hough domain.  To take into account fluctuations of 
handwritten text lines, i.e. the fact that units within a text line are not perfectly aligned, two 
hypotheses are considered for each alignment and an alignment is formed from units of the 
cell structure of a primary cell.  
 13  
Fig. 9.  Hypothesized cells (ρ0, θ0) and (ρ1, θ1) in Hough space. Each peak corresponds to perfectly aligned units. 
An alignment is composed of units belonging to a cluster of cells (the cell structure) around a primary cell. 
A cell structure of a cell (ρ, θ) includes all the cells lying in a cluster centered around (ρ, θ). 
Consider the cell (ρ0, θ0) having the greatest count of units. A second hypothesis (ρ1, θ1) is 
searched in the cell structure of (ρ0, θ0).  The alignment chosen between these two hypotheses 
is the strongest one, i.e. the one which includes the highest number of units in its cell 
structure. And the corresponding cell (ρ0, θ0) or (ρ1, θ1) is the primary cell (Fig. 9).  
However, actual text lines rarely correspond to alignments with the highest number of units as 
crossing alignments (from top to bottom for writing in horizontal direction) must contain 
more units than actual text lines. A potential alignment is validated (or invalidated) using 
contextual information, i.e. considering its internal and external neighbors. An internal 
neighbor of a unit j is a within-Hough alignment neighbor. An external neighbor is a out of 
Hough alignment neighbor which lies within a circle of radius δj from unit j. Distance δj is the 
average distance of the internal neighbor distances from unit j. To be validated, a potential 
alignment may contain fewer external units than internal ones. This enables the rejection of 
alignments which have no perceptual relevance. This method can extract oriented text lines 
and sloped annotations under the assumption that such lines are almost straight (Fig. 10).  
(ρ0, θ0)
(ρ1, θ1)
#units
(ρ0, θ0)
(ρ1, θ1)
#units
 14  
Fig. 10. Text lines extracted on an autograph of Miguel Angel Asturias. The orientations of traced lines 
correspond to those of the primary cells found in Hough space. 
The Hough transform can also be applied to fluctuating lines of handwritten drafts such as in 
Pu and Shi [45]. The Hough transform is first applied to minima points (units) in a vertical 
strip on the left of the image. The alignments in the Hough domain are searched starting from 
a main direction, by grouping cells in an exhaustive search in 6 directions. Then a moving 
window, associated with a clustering scheme in the image domain, assigns the remaining units 
to alignments.  The clustering scheme (Natural Learning Algorithm) allows the creation of 
new lines starting in the middle of the page.  
3.6 Repulsive-Attractive network method 
An approach based on attractive-repulsive forces is presented in Oztop et al. [40]. It works 
directly on grey-level images and consists in iteratively adapting the y-position of a 
predefined number of baseline units. Baselines are constructed one by one from the top of the 
image to bottom.  Pixels of the image act as attractive forces for baselines and already 
extracted baselines act as repulsive forces.  The baseline to extract is initialized just under the 
previously examined one, in order to be repelled by it and attracted by the pixels of the line 
below (the first one is initialized in the blank space at top of the document). The lines must 
have similar lengths. The result is a set of pseudo-baselines, each one passing through word 
bodies (Fig. 11). The method is applied to ancient Ottoman document archives and Latin 
texts.  
 15  
Fig. 11. Pseudo baselines extracted by a Repulsive-Attractive network on an Ancient Ottoman text (reprinted 
from Oztop et al. [40] Copyright (1999) with permission from Elsevier). 
3.7 Stochastic method 
We present here a method based on a probabilistic Viterbi algorithm (Tseng and Lee 
[56]), which derives non-linear paths between overlapping text lines. Although this method 
has been applied to modern Chinese handwritten documents, this principle could be enlarged 
to historical documents which often include overlapping lines. Lines are extracted through 
hidden Markov modeling. The image is first divided into little cells (depending on stroke 
width), each one corresponding to a state of the HMM (Hidden Markov Model). The best 
segmentation paths are searched from left to right; they correspond to paths which do not 
cross lots of black points and which are as straight as possible.  However, the displacement in 
the graph is limited to immediately superior or inferior grids. All best paths ending at each y 
location of the image are considered first. Elimination of some of these paths uses a quality 
threshold T: a path whose probability is less than T is discarded. Shifted paths are easily 
eliminated (and close paths are removed on quality criteria). The method succeeds when the 
ground truth path between text lines is slightly changing along the y-direction (Fig. 12). In the 
case of touching components, the path of highest probability will cross the touching 
component at points with as less black pixels as possible. But the method may fail if the 
contact point contains a lot of black pixels.  
Fig. 12. Segmentation paths obtained by a stochastic method (reprinted from Tseng and Lee [56], Copyright 
(1999) with permission from Elsevier). 
 16  
3.8  Processing of overlapping and touching components 
Overlapping and touching components are the main challenges for text line extractions since 
no white space is left between lines. Some of the methods surveyed above do not need to 
detect such components because they extract only baselines (3.4, 3.6), or because in the 
method itself some criteria make paths avoid crossing black pixels (c.f. Section 3.7). This 
section only deals with methods where ambiguous components (overlapping or touching) are 
actually detected before, during or after text line segmentation 
Such criteria as component size, the fact that the component belongs to several alignments, or 
on the contrary to no alignment, can be used for detecting ambiguous components. Once the 
component is detected as ambiguous, it must be classified into three categories: the 
component is an overlapping component which belongs to the upper (resp. lower) alignment, 
the component is a touching component which has to be decomposed into several parts (two 
or more parts, as components may belong to three or more alignments in historical 
documents).  The separation along the vertical direction is a hard problem which can be done 
roughly (horizontal cut), or more accurately by analysing stroke contours and referring to 
typical configurations (Fig. 13). 
Fig. 13. Set of typical overlapping configurations (adapted from Piquin et al. [44]). 
The grouping technique presented in Section 3.4 detects an ambiguous component during the 
grouping process when a conflict occurs between two alignments [28] [29]. A set of rules is 
applied to label the component as overlapping or touching. The ambiguous component 
extends in each alignment region. The rules use as features the density of black pixels of the 
component in each alignment region, alignment proximity and contextual information 
(positions of both alignments around the component). An overlapping component will be 
assigned to only one alignment. And the separation of a touching component is roughly 
performed by drawing a horizontal frontier segment. The frontier segment position is decided 
by analysing the vertical projection profile of the component. If the projection profile includes 
two peaks, the cut will be done middle way from them, as in Figure 14. Else the component 
will be cut into two equal parts. 
 17  
Fig. 14. Touching component separated  in a ‘Lettre de Remission’. 
In Likforman-Sulem et al. [30], touching and overlapping components are detected after the 
text line extraction process described in Section 3.5. These components are those which are 
intersected by at least two different lines (ρ,θ) corresponding to primary cells of validated 
alignments.  
In Zahour et al. [58][59], the document page is first cut into eight equal columns. A 
projection-profile is performed on each column. In each histogram, two consecutive minima 
delimit a text block. In order to detect touching and overlapping components, a k-means 
clustering scheme is used to classify the text blocks so extracted into three classes: big, 
average, small. Overlapping components necessarily belong to big physical blocks. All the 
overlapping cases are found in the big text blocks class. All the “one line” blocks are grouped 
in the average block text class. A second k-means clustering scheme finds the actual inter-line 
blocks; put together with the “one line” block size, this determines the number of pieces a 
large text block must be cut into (cf. Fig. 16).  
A similar method such as the one presented above is applied to Bangla handwriting Indian 
documents in Pal and Datta [41]. The document is divided into vertical strips. Profile cuts 
within each strip are computed to obtain anchor points of segmentation (PSLs) which do not 
cross any black pixels. These points are grouped through strips by neighboring criteria. If no 
segmentation point is present in the adjacent strip, the baseline is extended near the first black 
pixel encountered which belongs to an overlapping or touching component. This component 
is classified as overlapping or touching by analysing its vertical extension (upper, lower) from 
each side of the intersection point. An empirical rule classifies the component. In the touching 
case, the component is horizontally cut at the intersection point (Fig. 15). 
Fig.15. Overlapping components separated (circle) and touching component separated into two parts (rectangle) 
in Bangla writing (from Pal and Datta [41], © [2003] IEEE). 
 18  
Some solutions for separation of units belonging to several text lines can be found also in the 
case of mail pieces and handwritten databases where efforts have been made for recognition 
purposes [44] [7]. In the work of Piquin et al. [44], separation is made from the skeleton of 
touching characters and the use of a dictionary of possible touching configurations (Fig. 13). 
In Bruzzone and Coffetti [7], the contact point between ambiguous strokes is detected and 
processed from their external border. An accurate analysis of the contour near the contact 
point is performed in order to separate the strokes according to two registered configurations: 
a loop in contact with a stroke, or two loops in contact. In simple cases of handwritten pages 
(Marti  and Bunke [35]), the center of gravity of the connected component is used either to 
associate the component to the current line or to the following line, or to cut the component 
into two parts. This works well if the component is a single character. It may fail if the 
component is a word, or part of a word, or even several words.  
3.9 Non Latin documents 
The inter-line space in Latin documents is filled with single dots, ascenders and descenders. 
The Arabic script is connected and cursive. Large loops are present in the inter-line space and 
ancient Arabic documents include diacritical points [1]. In the Hebrew squared writing, the 
baseline is situated on top of characters. Documents such as decorated Bibles, prayer books 
and scientific treatises include diacritical points which represent vowels. Ancient Hebrew 
documents may include decorated words but no decorated initials as there is no upper/lower 
case character concept in this script. In the alphabets of some Indian scripts (like Devnagari, 
Bangla and Gurumukhi), many basic characters have an horizontal line (the head line) in the 
upper part [42]. In Bangla and Telugu text, touching and overlapping occur frequently [23].To 
date, the published studies on historical documents concern Arabic and Hebrew. Work about 
Chinese and Bangla Indian writings on good quality documents have been already mentioned 
in Sections 3.7 and 3.8: they should be also suitable to ancient documents as they include 
local processing.  
3.9.1 Ancient Arabic documents 
Figure 1 is a handwritten page extracted from a book of the Tunisian National Library. The 
writing is dense and inter-line space is faint. Several consecutive lines are often connected by 
one character at least, and the overlapping situations are obvious. Baseline waving produces 
various text orientations.  
The method developed in Zahour et al. [59] begins with the detection of overlapping and 
touching components presented in §3.8, using a two-stage clustering process which separates 
big blocks including several lines into several parts.  Blocks are then linked by neighborhood 
using the y coordinates. Figure 16 shows line separators using the clustering technique 
recursively, as described in Section  3.8. 
 19  
Fig.  16.  Text line segmentation of the ancient Arabic handwritten document in Fig. 1. 
3.9.1  Ancient Hebrew documents 
The manuscripts studied in Likforman-Sulem et al. [27], are written in Hebrew, in a so-called 
squared writing as most characters are made of horizontal and vertical strokes. They are 
reproducing the biblical text of the Pentateuch. Characters are calligraphed by skilled scribes 
with a quill or a calamus. The Scrolls, intended to be used in the synagogue, do not include 
diacritics. Characters and words are written properly separated but digitization make some 
characters touch. Cases of overlapping components occur as characters such as Lamed, Kaf, 
and final letters include ascenders and descenders. Since the majority of characters are 
composed of one connected component, it is more convenient to perform text line 
 20  
segmentation from connected components units. Fig. 17 shows the resulting segmentation 
with the Hough-based method presented in Section 3.5. 
Fig. 17. Text line segmentation of a Hebrew document (Scroll). 
 21  
 Table 1. Text line segmentation methods suitable for historical documents 
Authors Description Line 
Description 
Writing 
Type 
Units Suitable for Project/ 
Documents 
[Antonacopoulos 
and Karatzas, 
2004] 
projection 
profiles 
linear paths Latin 
printed 
pixels separated 
lines 
Memorial/person
al records(World 
War II) 
[Calabretto and 
Bozzi, 1998] 
projection 
profiles (gray 
level image) 
linear paths cursive 
handwriting 
pixels separated 
lines 
Bambi/italian 
manuscripts (16th 
century) 
[Feldbach and 
Tönnies, 2001] 
grouping  
method 
baselines  cursive 
handwriting
minima 
points 
fluctuating 
lines 
Church registers 
(18th, 19th 
century) 
[He and Downton, 
2003] 
projections 
(RXY cuts) 
linear paths Latin 
printed and 
handwriting
pixels separated 
lines 
Viadocs/ Natural 
History Cards 
[Lebourgeois et al., 
2001] 
smearing 
(accumulated 
gradients) 
clusters Latin 
printed  
pixels separated 
lines 
Debora/books 
(16th century) 
[Likforman-Sulem 
and Faure, 1994] 
grouping  strings Latin 
handwriting 
connected 
components
fluctuating 
lines  
Philectre/ 
authorial 
manuscripts 
[Likforman-Sulem 
et al., 1995] 
Hough 
transform, 
(hypothesis-
validation 
scheme) 
strings Latin 
handwriting 
connected 
components
different 
straight line 
directions 
Philectre/ 
authorial 
manuscripts, 
manuscripts of 
the 16th century 
[Oztop et al., 1997] repulsive -
attractive 
network 
baselines Arabic and 
Latin  
handwriting
pixels (gray 
levels) 
fluctuating 
lines (same 
size) 
ancient Ottoman 
documents 
[Pal and Datta, 
2003] 
piecewise 
projections 
piecewise 
linear paths 
Bangla    
handwriting  
segmentatio
n points 
overlapping/ 
touching lines 
Indian 
handwritten 
documents 
[Pu and Shi, 1998] Hough 
transform 
(moving 
window)  
clusters Latin 
handwriting
minima 
points 
fluctuating 
lines 
handwritten 
documents 
[Shapiro et al., 
1993] 
projection 
profiles 
linear paths Latin 
handwriting
pixels skewed 
separated 
lines 
handwritten 
documents 
[Shi and 
Govindaraju, 2004] 
smearing 
(fuzzy run 
length) 
cluster Latin 
handwriting
pixels straight 
touching lines 
Newton, Galileo 
manuscripts 
[Tseng and Lee, 
1999 ] 
stochastic 
(probabilistic 
Viterbi 
algorithm) 
non linear 
paths 
Chinese 
handwriting
pixels overlapping 
lines 
handwritten 
documents 
[Zahour et al., 
2004] 
piecewise 
projection and 
k-means 
clustering 
piecewise 
linear paths 
Arabic 
handwriting
text blocks overlapping/ 
touching 
lines. 
ancient Arabic 
documents        
 22  
4. Discussion and concluding remarks 
An overview of text line segmentation methods developed within different projects is 
presented in Table 1. The achieved taxonomy consists in six major categories. They are listed 
as: projection-based, smearing, grouping, Hough-based, repulsive-attractive network and 
stochastic methods. Most of these methods are able to face some image degradations and 
writing irregularities specific to historical documents, as shown in the last column of Table 1.  
Projection, smearing and Hough-based methods, classically adapted to straight lines and 
easier to implement, had to be completed and enriched by local considerations (piecewise 
projections, clustering in Hough space, use of a moving window, ascender and descender 
skipping), so as to solve some problems including: line proximity, overlapping or even 
touching strokes, fluctuating close lines, shape fragmentation occurrences.  The stochastic 
method (achieved by the Viterbi decision algorithm) is conceptually more robust, but its 
implementation requires great care, particularly the initialization phase. As a matter of fact, 
text-line images are initially divided into mxn grids (each cell being a node), where the values 
of the critical parameters m and n are to be determined according to the estimated average 
stroke width in the images. Representing a text line by one or more baselines (RA method, 
minima point grouping) must be completed by labeling those pixels not connected to, or 
between the extracted baselines. The recurrent nature of the repulsive-attractive method may 
induce cascading detecting errors following a unique false or bad line extraction.  
Projection and Hough-based methods are suitable for clearly separated lines. Projection-based 
methods can cope with few overlapping or touching components, as long text lines smooth 
both noise and overlapping effects. Even in more critical cases, classifying the set of blocks 
into “one line width” blocks and “several lines width” blocks allows the segmentation process 
to get statistical measures so as to segment more surely the “several lines width”  blocks. As a 
result, the linear separator path may cross overlapping components. However, more accurate 
segmentation of the overlapping components can be performed after getting the global or 
piecewise straight separator, by looking closely at the so crossed strokes. The stochastic 
method naturally avoids crossing overlapping components  (if they are not too close): the 
resulting non linear paths turn around obstacles. When lines are very close, grouping methods 
encounter a lot of conflicting configurations. A wrong decision in an early stage of the 
grouping results in errors or incomplete alignments.  In case of touching components, making 
an accurate segmentation requires additional knowledge (compiled in a dictionary of possible 
configurations or represented by logical or fuzzy rules).    
    Concerning text line fluctuations, baseline-based representations seem to fit naturally. 
Methods using straight line-based representations  must be modified as previously to give non 
linear results (by piecewise projections or neighboring considerations in Hough space). The 
more fluctuating the text line, the more refined local criteria must be. Accurate locally 
oriented processing and careful grouping rules make smearing and grouping methods 
convenient. The stochastic methods also seem suited, for they can generate non linear 
segmentation paths to separate overlapping characters, and even more to derive non linear 
cutting paths from touching characters by identifying the shortest paths. 
 23  
Pixel based methods are naturally robust at dealing with writing fragmentation. But, as a 
consequence of writing fragmentation, when units become fragmented, sub-units may be 
located far from the baseline. Spurious characteristic points are then generated, disturbing 
alignment and implying a loss of accuracy, or more, a wrong final representation.  
Quantitative assessment of performance is not generally yielded by the authors of the 
methods; when it is given, this is on a reduced set of documents. As for all segmentation 
methods, ground truth data are harder to obtain than for classification methods. For instance 
the ground truth for the real baseline may be hard to assess. Text line segmentation is often a 
step in the recognition algorithm and the segmentation task is not evaluated in isolation. To 
date, no general study has been carried out to compare the different methods. Text line 
representations differ and methods are generally tuned to a class of documents.  
Analysis of historical document images is a relatively new domain. Text line segmentation 
methods have been developed within several projects which perform transcript mapping, 
authentication, word mapping or word recognition. As the need for recognition and mapping 
of handwritten material increases, text line segmentation will be used more and more. 
Contrary to printed modern documents, a historical document has unique characteristics due 
to style, artistic effect and writer skills. There is no universal segmentation method which can 
fit all these documents. The techniques presented here have been proposed to segment 
particular sets of documents. They can however be generalized to other documents with 
similar characteristics, with parameter tuning that depends on script size, stroke width and 
average spacing.  
The major difficulty consists in obtaining a precise text line, with all descenders and 
ascenders segmented for accessing isolated words. As segmentation and recognition are 
dependent tasks, the exact segmentation of touching pieces of writing may need some 
recognition, or knowledge about the possible touching configurations. Text line segmentation 
algorithms will benefit from automatic extraction of document characteristics leading to an 
easier adaptation to the document under study. 
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  There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specific fields are in use today. For all these tasks, a
major step is document segmentation into text lines. Because of the low quality
and the complexity of these documents (background noise, artifacts due to
aging, interfering lines),automatic text line segmentation remains an open
research field. The objective of this paper is to present a survey of existing
methods, developed during the last decade, and dedicated to documents of
historical interest.

Introduction  
Text line extraction is generally seen as a preprocessing step for tasks such as document 
structure extraction, printed character or handwriting recognition. Many techniques have been 
developed for page segmentation of printed documents (newspapers, scientific journals, 
magazines, business letters) produced with modern editing tools [57] [38] [14] [39] [2]. The 
segmentation of handwritten documents has also been addressed with the segmentation of 
address blocks on envelopes and mail pieces [9] [10] [15][48], and for authentication or 
recognition purposes [53] [60]. More recently, the development of handwritten text databases 
(IAM database, [34]) provides new material for handwritten page segmentation.  
Ancient and historical documents, printed or handwritten, strongly differ from the documents 
mentioned above because layout formatting requirements were looser. Their physical 
structure is thus harder to extract. In addition, historical documents are of low quality, due to 
aging or faint typing. They include various disturbing elements such as holes, spots, writing 
from the verso appearing on the recto, ornamentation, or seals. Handwritten pages include 
narrow spaced lines with overlapping and touching components. Characters and words have 
unusual and varying shapes, depending on the writer, the period and the place concerned. The 
vocabulary is also large and may include unusual names and words. Full text recognition is in 
most cases not yet available, except for printed documents for which dedicated OCR can be 
developed.  
However, invaluable collections of historical documents are already digitized and indexed for 
consulting, exchange and distant access purposes which protect them from direct 
manipulation. In some cases, highly structured editions have been established by scholars. But 
a huge amount of documents are still to be exploited electronically. To produce an electronic 
searchable form, a document has to be indexed. The simplest way of indexing a document 
consists in attaching its main characteristics such as date, place and author (the so called 
‘metadata’). Indexing can be enhanced when the document structure and content are 
exploited. When a transcription (published version, diplomatic transcription) is available, it 
can be attached to the digitized document: this allows users to retrieve documents from 
textual queries.  
Since text based representations do not reflect the graphical features of such documents, a 
better representation is obtained by linking the transcription to the document image. A direct 
correspondence can then be established between the document image and its content by 
text/image alignment techniques [55]. This allows the creation of indexes where the position 
of each word can be recorded, and of links between both representations. Clicking on a word 
on the transcription or in the index through a GUI allows users to visualize the corresponding 
image area and vice versa. To make such queries possible for handwritten sources of literary 
works, several projects have been carried out under EU and National Programs: for instance 
the so-called ‘philological workstation’ Bambi [6][8] and within the Philectre reading and 
editing environment [47]. The document analysis embedded in such systems provides tools to 
search for blocks, lines and words, and may include a dedicated handwriting recognition 
system. Interactive tools are generally offered for segmentation and recognition correction 
purposes. Several projects also concern printed material: Debora [5] and Memorial [3]. Partial 
or complete logical structure can also be extracted by document analysis and corrected with 
GUI as in the Viadocs project [11][18]. However, document structure can also be used when 
no transcription is available. Word spotting techniques [22] [55] [46] can retrieve similar 
words in the image document through an image query. When words of the image document 
are extracted by top down segmentation, which is generally the case, text lines are extracted 
first.  
                                                                 
Fig. 1.  Examples of historical documents a) Provencal medieval manuscript.  b) one page from De Gaulle’s 
diaries c) an ancient Arabic document from Tunisian Archives. 
The authentication of manuscripts in the paleographic sense can also make use of document 
analysis and text line extraction. Authentication consists in retrieving writer characteristics 
independently from document content. It generally consists in dating documents, localizing 
the place where the document was produced, identifying the writer by using characteristics 
and features extracted from blank spaces, line orientations and fluctuations, word or character 
shapes [43] [27] [4]. 
Page segmentation into text lines is performed in most tasks mentioned above and overall 
performance strongly relies on the quality of this process. The purpose of this article is to 
survey the efforts made for historical documents on the text line segmentation task. Section 2 
describes the characteristics of text line structures in historical documents and the different 
ways of defining a text line. Preprocessing of document images (gray level, color or black and 
white) is often necessary before text line extracting to prune superfluous information (non 
textual elements, textual elements from the verso) or to correctly binarize the image.  This 
problem is addressed in Section 3.1. In Sections 3.2-3.7 we survey the different approaches to 
segment the clean image into text lines. A taxonomy is proposed, listed as projection profiles, 
smearing, grouping, Hough-based, repulsive-attractive network and stochastic methods. The 
majority of these techniques have been developed for the projects on historical documents 
mentioned above. In Section 3.8, we address the specific problem of overlapping and 
touching components. Concluding remarks are given in Section 4.  
Fig. 2.  Reference lines and interfering lines with overlapping and touching components. 
2. Characteristics and representation of text lines  
To have a good idea of the physical structure of a document image, one only needs to look at 
it from a certain distance: the lines and the blocks are immediately visible. These blocks 
consist of columns, annotations in margins, stanzas, etc... As blocks generally have no 
rectangular shape in historical documents, the text line structure becomes the dominant 
physical structure. We first give some definitions about text line components and text line 
segmentation. Then we describe the factors which make this text line segmentation hard. 
Finally, we describe how a text line can be represented.  
2.1 Definitions 
baseline: fictitious line which follows and joins the lower part of the character bodies in a text 
line (Fig. 2) 
median line: fictitious line which follows and joins the upper part of the character bodies in a 
text line. 
upper line: fictitious line which joins the top of ascenders. 
lower line: fictitious line which joins the bottom of descenders. 
overlapping components: overlapping components are descenders and ascenders located in 
the region of an adjacent line (Fig. 2).   
touching components: touching components are ascenders and descenders belonging to 
consecutive lines which are thus connected. These components are large but hard to 
discriminate before text lines are known.  
text line segmentation: text line segmentation is a labeling process which consists in assigning 
the same label to spatially aligned units (such as pixels, connected components or 
characteristic points). There are two categories of text line segmentation approaches: 
searching for (fictitious) separating lines or paths, or searching for aligned physical units. The 
choice of a segmentation technique depends on the complexity of the text line structure of the 
document. 
2.2 Influence of author style 
baseline fluctuation: the baseline may vary due to writer movement. It may be straight, 
straight by segments, or curved.  
line orientations: there may be different line orientations, especially on authorial works where 
there are corrections and annotations.   
line spacing: lines that are rather widely spaced lines are easy to find. The process of 
extracting text lines grows more difficult as interlines are narrowing; the lower baseline of the 
first line is becoming closer to the upper baseline of the second line; also, descenders and 
ascenders start to fill the blank space left for separating two adjacent text lines (Fig. 3).  
insertions: words or short text lines may appear between the principal text lines, or in the 
margins.  
2.3 Influence of poor image quality 
imperfect preprocessing: smudges, variable background intensity and the presence of seeping 
ink from the other side of the document make image preprocessing particularly difficult and 
produce binarization errors.  
stroke fragmentation and merging: punctuation, dots and broken strokes due to low-quality 
images and/or binarization may produce many connected components; conversely, words, 
characters and strokes may be split into several connected components. The broken 
components are no longer linked to the median baseline of the writing and become ambiguous 
and hard to segment into the correct text line (Fig. 3). 
2.4 Text line representation 
separating paths and delimited strip: separating lines (or paths) are continuous fictitious lines 
which can be uniformly straight, made of straight segments, or of curving joined strokes. The 
delimited strip between two consecutive separating lines receives the same text line label. So 
the text line can be represented by a strip with its couple of separating lines (Fig. 4). 
clusters: clusters are a general set-based way of defining text lines. A label is associated with 
each cluster. Units within the same cluster belong to the same text line. They may be pixels, 
connected components, or blocks enclosing pieces of writing. A text line can be represented 
by a list of units with the same label. 
Fig. 3.  The three main axes of document complexity for text line segmentation. 
Fig. 4. Various text line representations: paths, strings and baselines. 
strings: strings are lists of spatially aligned and ordered units. Each  string represents one text 
line.  
baselines: baselines follow line fluctuations but partially define a text line. Units connected to 
a baseline are assumed to belong to it. Complementary processing has to be done to cluster 
non-connected units and touching components. 
line    proximity
line fluctuation
writing fragmentation
line    proximity
line fluctuation
writing fragmentation
3. Text line segmentation  
Printed historical documents belong to a large period from 16th to 20th centuries (reports, 
ancient books, registers, card archives). Their printing may be faint, producing writing 
fragmentation artifacts. However, text lines are still enclosed in rectangular areas. After the 
text part has been extracted and restored, top-down and smearing techniques are generally 
applied for text line segmentation. A large proportion of historical documents are handwritten: 
scrolls, registers, private and official letters, authorial drafts. The type of writing differs 
considerably from one document to another. It can be calligraphed or cursive; various styles 
can be observed (Fig. 1). In the context of cursive handwriting, statistical information about 
line spacing and line orientation is hard to capture. Several techniques, which take into 
account handwriting and layout irregularities, as well as local and global characteristics of the 
text lines, have been developed 
3.1 Preprocessing 
Text line extraction would ideally process document images without background noise and 
without non-textual elements; the writing would be well contrasted with as little 
fragmentation as possible. In reality, preprocessing is often necessary. Although 
preprocessing has to be accurately adapted to each document and to its characteristics, we 
shortly describe here some preprocessing techniques that can be performed before text line 
extraction.  
Non-textual elements around the text such as book bindings, book sides, parts of fingers 
(thumb marks from someone holding the book open f.i.) should be removed upon criteria such 
as position and intensity level. On the document itself, holes, stains, may be removed by high-
pass filtering [12]. Other non-textual elements (stamps, seals) but also ornamentation, 
decorated initials, can be removed using knowledge about the shape, the color or the position 
of these elements [17]. Extracting text from figures (text segmentation) can also be performed 
on texture grounds [20][36] or by morphological filters [16][37]. Linear graphical elements 
such as big crosses (called “St Andre’s crosses”) appear in some of Flaubert’s manuscripts. 
Removing these elements is performed through GUI by Kalman filtering in [31].   
Textual but unwanted elements such as the writing on the verso (bleed through text) may be 
removed by filtering and wavelet techniques [24][54][32] and by combining the verso image 
(the reverse side image) with the recto one (front side image).  
Binarization, if necessary, can be performed by global or local thresholding. Global 
thresholding algorithms are not generally applicable to historical documents, due to 
inhomogeneous background. Thus, global thresholding results in severe deterioration in the 
quality of the segmented document image. Several local thresholding techniques have already 
been proposed to partially overcome such difficulties [21]. These local methods determine the 
threshold values based on the local properties of an image, e.g. pixel-by-pixel or region-by-
region, and yield relatively better binarization results when compared with global 
thresholding methods. Writing may be faint so that over-segmentation or under-segmentation 
may occur. The integral ratio technique [52] is a two-stage segmentation technique adapted to 
this problem. Background normalization [51] can be performed before binarization in order to 
find a global threshold more easily.  
3.2 Projection–based methods  
Projection-profiles are commonly used for printed document segmentation. This technique 
can also be adapted to handwritten documents with little overlap. The vertical projection-
profile is obtained by summing pixel values along the horizontal axis for each y value. From 
the vertical profile, the gaps between the text lines in the vertical direction can be observed 
(Fig. 5). 
yxfyprofile
),()(   
The vertical profile is not sensitive to writing fragmentation. Variants for obtaining a profile 
curve may consist in projecting black/white transitions such as in Marti and Bunke [35] or the 
number of connected components, rather than pixels. The profile curve can be smoothed, e.g. 
by a Gaussian or median filter to eliminate local maxima [33]. The profile curve is then 
analysed to find its maxima and minima. There are two drawbacks: short lines will provide 
low peaks, and very narrow lines, as well as those including many overlapping components 
will not produce significant peaks. In case of skew or moderate fluctuations of the text lines, 
the image may be divided into vertical strips and profiles sought inside each strip (Zahour et 
al. [58]). These piecewise projections are thus a means of adapting to local fluctuations within 
a more global scheme.  
In Shapiro et al.[49], the global orientation (skew angle) of a handwritten page is first 
searched by applying a Hough transform on the entire image. Once this skew angle is 
obtained, projections are achieved along this angle. The number of maxima of the profile give 
the number of lines. Low maxima are discarded on their value, which is compared to the 
highest maxima. Lines are delimited by strips, searching for the minima of projection profiles 
around each maxima. This technique has been tested on a set of 200 pages within a word 
segmentation task.  
In the work of Antonacopoulos and Karatzas [3], each minimum of the profile curve is a 
potential segmentation point.  Potential points are then scored according to their distance to 
adjacent segmentation points. The reference distance is obtained from the histogram of 
distances between adjacent potential segmentation points. The highest scored segmentation 
point is used as an anchor to derive the remaining ones. The method is applied to printed 
records of the second World War which have regularly spaced text lines. The logical structure 
is used to derive the text regions where the names of interest can be found. 
Fig. 5.  Vertical projection-profile extracted on an autograph of Jean-Paul Sartre. 
The RXY cuts method applied in He and Downton [18], uses alternating projections along the 
X and the Y axis. This results in a hierarchical tree structure. Cuts are found within white 
spaces. Thresholds are necessary to derive inter-line or inter-block distances. This method can 
be applied to printed documents (which are assumed to have these regular distances) or well 
separated handwritten lines. 
3.3 Smearing methods 
For printed and binarized documents, smearing methods such as the Run-Length Smoothing 
Algorithm (Wong et al. [57]) can be applied. Consecutive black pixels along the horizontal 
direction are smeared: i.e. the white space between them is filled with black pixels if their 
distance is within a predefined threshold.  The bounding boxes of the connected components 
in the smeared image enclose text lines.  
A variant of this method adapted to gray level images and applied to printed books from the 
sixteenth century consists in accumulating the image gradient along the horizontal direction 
(LeBourgeois [25]). This method has been adapted to old printed documents within the 
Debora project [26]. For this purpose, numerous adjustments in the method concern the 
tolerance for character alignment and line justification.  
Text line patterns are found in the work of Shi and Govindaraju [50] by building a fuzzy run 
length matrix. At each pixel, the fuzzy run-length is the maximal extent of the background 
along the horizontal direction. Some foreground pixels may be skipped if their number does 
not exceed a predefined value. This matrix is threshold to make pieces of text lines appear 
without ascenders and descenders (Fig. 6).  Parameters have to be accurately and dynamically 
tuned.  
3.4 Grouping methods  
These methods consist in building alignments by aggregating units in a bottom-up strategy. 
The units may be pixels or of higher level, such as connected components, blocks or other 
features such as salient points. Units are then joined together to form alignments. The joining 
scheme relies on both local and global criteria, which are used for checking local and global 
consistency respectively. 
 10  
Fig. 6 Text line patterns extracted from a letter of Georges Washington (reprinted from Shi and Govindaraju 
[50], © [2004] IEEE). Foreground pixels have been smeared along the horizontal direction. 
Contrary to printed documents, a simple nearest-neighbor joining scheme would often fail to 
group complex handwritten units, as the nearest neighbor often belongs to another line. The 
joining criteria used in the methods described below are adapted to the type of the units and 
the characteristics of the documents under study. But every method has to face the following: 
- initiating alignments: one or several seeds for each alignment. 
- defining a unit’s neighborhood for reaching the next unit. It is generally a rectangular or 
angular area (Fig. 7).  
- solving conflicts. As one unit may belong to several alignments under construction, a choice 
has to be made: discard one alignment or keep both of them, cutting the unit into several parts. 
Hence, these methods include one or several quality measures which ensure that the text line 
under construction is of good quality. When comparing the quality measures of two 
alignments in conflict, the alignment of lower quality can be discarded (Fig. 7).  Also, during 
the grouping process, it is possible to choose between the different units that can be 
aggregated within the same neighborhood by evaluating the quality of each of the so-formed 
alignments.  
 11  
Fig. 7. Angular and rectangular neighborhoods from point and rectangular units (left). Neighborhood defined by 
a cluster of units (upright).  Two alignments A and B in conflict: a quality measure will choose A and discard B 
(down right). 
Quality measures generally include the strength of the alignment, i.e. the number of units 
included. Other quality elements may concern component size, component spacing, or a 
measure of the alignment’s straightness.  
Fig. 8. Text lines extracted on Church Registers (reprinted from Feldbach [12] with permission from the author). 
Likforman-Sulem and Faure have developed in [28] an iterative method based on perceptual 
grouping for forming alignments, which has been applied to handwritten pages, author drafts 
and historical documents [29][47]. Anchors are detected by selecting connected components 
elongated in specific directions (0°, 45°, 90°, 125°). Each of these anchors becomes the seed 
of an alignment. First, each anchor, then each alignment, is extended to the left and to the 
right. This extension uses three Gestalt criteria for grouping components: proximity, similarity 
and direction continuity. The threshold is iteratively incremented in order to group 
components within a broader neighborhood until no change occurs. Between each iteration, 
alignment quality is checked by a quality measure which gives higher rates to long alignments 
including anchors of the same direction. A penalty is given when the alignment includes 
anchors of different directions. Two alignments may cross each other, or overlap. A set of 
 12  
rules is applied to solve these conflicts taking into account the quality of each alignment and 
neighboring components of higher order (Fig. 14). 
In the work of Feldbach and Tönnies [12][13], body baselines are searched in Church 
Registers images. These documents include lots of fluctuating and overlapping lines. 
Baselines units are the minima points of the writing  (obtained here from the skeleton). First 
basic line segments (BLS) are constructed, joining each minima point to its neighbors.  This 
neighborhood is defined by an angular region (+-20°) for the first unit grouped, then by a 
rectangular region enclosing the points already joined for the remaining ones. Unwanted basic 
segments are found from minima points detected in descenders and ascenders. These 
segments may be isolated or in conflict with others. Various heuristics are defined to 
eliminate alignments on their size, or the local inter-line distance and on a quality measure 
which favours alignments whose units are in the same direction rather than nearer units but 
positioned lower or higher than the current direction. Conflicting alignments can be 
reconstructed depending on the topology of the conflicting alignments. The median line is 
searched from the baseline and from maxima points (Fig. 8). Pixels lying within a given 
baseline and median line are clustered in the corresponding text line, while ascenders and 
descenders are not segmented. Correct segmentation rates are reported between 90% and 97 
% with adequate parameter adjustment. The seven documents tested range from the 17th to the 
19th century. 
3.5 Methods based on the Hough transform 
The Hough transform is a very popular technique [19] for finding straight lines in images. In 
Likforman-Sulem et al. [30], a method has been developed on a hypothesis-validation 
scheme. Potential alignments are hypothesized in the Hough domain and validated in the 
Image domain. Thus, no assumption is made about text line directions (several may exist 
within the same page). The centroids of the connected components are the units for the Hough 
transform. A set of aligned units in the image along a line with parameters (ρ, θ) is included 
in the corresponding cell (ρ, θ) of the Hough domain. Alignments including a lot of units 
correspond to high peaked cells of the Hough domain.  To take into account fluctuations of 
handwritten text lines, i.e. the fact that units within a text line are not perfectly aligned, two 
hypotheses are considered for each alignment and an alignment is formed from units of the 
cell structure of a primary cell.  
 13  
Fig. 9.  Hypothesized cells (ρ0, θ0) and (ρ1, θ1) in Hough space. Each peak corresponds to perfectly aligned units. 
An alignment is composed of units belonging to a cluster of cells (the cell structure) around a primary cell. 
A cell structure of a cell (ρ, θ) includes all the cells lying in a cluster centered around (ρ, θ). 
Consider the cell (ρ0, θ0) having the greatest count of units. A second hypothesis (ρ1, θ1) is 
searched in the cell structure of (ρ0, θ0).  The alignment chosen between these two hypotheses 
is the strongest one, i.e. the one which includes the highest number of units in its cell 
structure. And the corresponding cell (ρ0, θ0) or (ρ1, θ1) is the primary cell (Fig. 9).  
However, actual text lines rarely correspond to alignments with the highest number of units as 
crossing alignments (from top to bottom for writing in horizontal direction) must contain 
more units than actual text lines. A potential alignment is validated (or invalidated) using 
contextual information, i.e. considering its internal and external neighbors. An internal 
neighbor of a unit j is a within-Hough alignment neighbor. An external neighbor is a out of 
Hough alignment neighbor which lies within a circle of radius δj from unit j. Distance δj is the 
average distance of the internal neighbor distances from unit j. To be validated, a potential 
alignment may contain fewer external units than internal ones. This enables the rejection of 
alignments which have no perceptual relevance. This method can extract oriented text lines 
and sloped annotations under the assumption that such lines are almost straight (Fig. 10).  
(ρ0, θ0)
(ρ1, θ1)
#units
(ρ0, θ0)
(ρ1, θ1)
#units
 14  
Fig. 10. Text lines extracted on an autograph of Miguel Angel Asturias. The orientations of traced lines 
correspond to those of the primary cells found in Hough space. 
The Hough transform can also be applied to fluctuating lines of handwritten drafts such as in 
Pu and Shi [45]. The Hough transform is first applied to minima points (units) in a vertical 
strip on the left of the image. The alignments in the Hough domain are searched starting from 
a main direction, by grouping cells in an exhaustive search in 6 directions. Then a moving 
window, associated with a clustering scheme in the image domain, assigns the remaining units 
to alignments.  The clustering scheme (Natural Learning Algorithm) allows the creation of 
new lines starting in the middle of the page.  
3.6 Repulsive-Attractive network method 
An approach based on attractive-repulsive forces is presented in Oztop et al. [40]. It works 
directly on grey-level images and consists in iteratively adapting the y-position of a 
predefined number of baseline units. Baselines are constructed one by one from the top of the 
image to bottom.  Pixels of the image act as attractive forces for baselines and already 
extracted baselines act as repulsive forces.  The baseline to extract is initialized just under the 
previously examined one, in order to be repelled by it and attracted by the pixels of the line 
below (the first one is initialized in the blank space at top of the document). The lines must 
have similar lengths. The result is a set of pseudo-baselines, each one passing through word 
bodies (Fig. 11). The method is applied to ancient Ottoman document archives and Latin 
texts.  
 15  
Fig. 11. Pseudo baselines extracted by a Repulsive-Attractive network on an Ancient Ottoman text (reprinted 
from Oztop et al. [40] Copyright (1999) with permission from Elsevier). 
3.7 Stochastic method 
We present here a method based on a probabilistic Viterbi algorithm (Tseng and Lee 
[56]), which derives non-linear paths between overlapping text lines. Although this method 
has been applied to modern Chinese handwritten documents, this principle could be enlarged 
to historical documents which often include overlapping lines. Lines are extracted through 
hidden Markov modeling. The image is first divided into little cells (depending on stroke 
width), each one corresponding to a state of the HMM (Hidden Markov Model). The best 
segmentation paths are searched from left to right; they correspond to paths which do not 
cross lots of black points and which are as straight as possible.  However, the displacement in 
the graph is limited to immediately superior or inferior grids. All best paths ending at each y 
location of the image are considered first. Elimination of some of these paths uses a quality 
threshold T: a path whose probability is less than T is discarded. Shifted paths are easily 
eliminated (and close paths are removed on quality criteria). The method succeeds when the 
ground truth path between text lines is slightly changing along the y-direction (Fig. 12). In the 
case of touching components, the path of highest probability will cross the touching 
component at points with as less black pixels as possible. But the method may fail if the 
contact point contains a lot of black pixels.  
Fig. 12. Segmentation paths obtained by a stochastic method (reprinted from Tseng and Lee [56], Copyright 
(1999) with permission from Elsevier). 
 16  
3.8  Processing of overlapping and touching components 
Overlapping and touching components are the main challenges for text line extractions since 
no white space is left between lines. Some of the methods surveyed above do not need to 
detect such components because they extract only baselines (3.4, 3.6), or because in the 
method itself some criteria make paths avoid crossing black pixels (c.f. Section 3.7). This 
section only deals with methods where ambiguous components (overlapping or touching) are 
actually detected before, during or after text line segmentation 
Such criteria as component size, the fact that the component belongs to several alignments, or 
on the contrary to no alignment, can be used for detecting ambiguous components. Once the 
component is detected as ambiguous, it must be classified into three categories: the 
component is an overlapping component which belongs to the upper (resp. lower) alignment, 
the component is a touching component which has to be decomposed into several parts (two 
or more parts, as components may belong to three or more alignments in historical 
documents).  The separation along the vertical direction is a hard problem which can be done 
roughly (horizontal cut), or more accurately by analysing stroke contours and referring to 
typical configurations (Fig. 13). 
Fig. 13. Set of typical overlapping configurations (adapted from Piquin et al. [44]). 
The grouping technique presented in Section 3.4 detects an ambiguous component during the 
grouping process when a conflict occurs between two alignments [28] [29]. A set of rules is 
applied to label the component as overlapping or touching. The ambiguous component 
extends in each alignment region. The rules use as features the density of black pixels of the 
component in each alignment region, alignment proximity and contextual information 
(positions of both alignments around the component). An overlapping component will be 
assigned to only one alignment. And the separation of a touching component is roughly 
performed by drawing a horizontal frontier segment. The frontier segment position is decided 
by analysing the vertical projection profile of the component. If the projection profile includes 
two peaks, the cut will be done middle way from them, as in Figure 14. Else the component 
will be cut into two equal parts. 
 17  
Fig. 14. Touching component separated  in a ‘Lettre de Remission’. 
In Likforman-Sulem et al. [30], touching and overlapping components are detected after the 
text line extraction process described in Section 3.5. These components are those which are 
intersected by at least two different lines (ρ,θ) corresponding to primary cells of validated 
alignments.  
In Zahour et al. [58][59], the document page is first cut into eight equal columns. A 
projection-profile is performed on each column. In each histogram, two consecutive minima 
delimit a text block. In order to detect touching and overlapping components, a k-means 
clustering scheme is used to classify the text blocks so extracted into three classes: big, 
average, small. Overlapping components necessarily belong to big physical blocks. All the 
overlapping cases are found in the big text blocks class. All the “one line” blocks are grouped 
in the average block text class. A second k-means clustering scheme finds the actual inter-line 
blocks; put together with the “one line” block size, this determines the number of pieces a 
large text block must be cut into (cf. Fig. 16).  
A similar method such as the one presented above is applied to Bangla handwriting Indian 
documents in Pal and Datta [41]. The document is divided into vertical strips. Profile cuts 
within each strip are computed to obtain anchor points of segmentation (PSLs) which do not 
cross any black pixels. These points are grouped through strips by neighboring criteria. If no 
segmentation point is present in the adjacent strip, the baseline is extended near the first black 
pixel encountered which belongs to an overlapping or touching component. This component 
is classified as overlapping or touching by analysing its vertical extension (upper, lower) from 
each side of the intersection point. An empirical rule classifies the component. In the touching 
case, the component is horizontally cut at the intersection point (Fig. 15). 
Fig.15. Overlapping components separated (circle) and touching component separated into two parts (rectangle) 
in Bangla writing (from Pal and Datta [41], © [2003] IEEE). 
 18  
Some solutions for separation of units belonging to several text lines can be found also in the 
case of mail pieces and handwritten databases where efforts have been made for recognition 
purposes [44] [7]. In the work of Piquin et al. [44], separation is made from the skeleton of 
touching characters and the use of a dictionary of possible touching configurations (Fig. 13). 
In Bruzzone and Coffetti [7], the contact point between ambiguous strokes is detected and 
processed from their external border. An accurate analysis of the contour near the contact 
point is performed in order to separate the strokes according to two registered configurations: 
a loop in contact with a stroke, or two loops in contact. In simple cases of handwritten pages 
(Marti  and Bunke [35]), the center of gravity of the connected component is used either to 
associate the component to the current line or to the following line, or to cut the component 
into two parts. This works well if the component is a single character. It may fail if the 
component is a word, or part of a word, or even several words.  
3.9 Non Latin documents 
The inter-line space in Latin documents is filled with single dots, ascenders and descenders. 
The Arabic script is connected and cursive. Large loops are present in the inter-line space and 
ancient Arabic documents include diacritical points [1]. In the Hebrew squared writing, the 
baseline is situated on top of characters. Documents such as decorated Bibles, prayer books 
and scientific treatises include diacritical points which represent vowels. Ancient Hebrew 
documents may include decorated words but no decorated initials as there is no upper/lower 
case character concept in this script. In the alphabets of some Indian scripts (like Devnagari, 
Bangla and Gurumukhi), many basic characters have an horizontal line (the head line) in the 
upper part [42]. In Bangla and Telugu text, touching and overlapping occur frequently [23].To 
date, the published studies on historical documents concern Arabic and Hebrew. Work about 
Chinese and Bangla Indian writings on good quality documents have been already mentioned 
in Sections 3.7 and 3.8: they should be also suitable to ancient documents as they include 
local processing.  
3.9.1 Ancient Arabic documents 
Figure 1 is a handwritten page extracted from a book of the Tunisian National Library. The 
writing is dense and inter-line space is faint. Several consecutive lines are often connected by 
one character at least, and the overlapping situations are obvious. Baseline waving produces 
various text orientations.  
The method developed in Zahour et al. [59] begins with the detection of overlapping and 
touching components presented in §3.8, using a two-stage clustering process which separates 
big blocks including several lines into several parts.  Blocks are then linked by neighborhood 
using the y coordinates. Figure 16 shows line separators using the clustering technique 
recursively, as described in Section  3.8. 
 19  
Fig.  16.  Text line segmentation of the ancient Arabic handwritten document in Fig. 1. 
3.9.1  Ancient Hebrew documents 
The manuscripts studied in Likforman-Sulem et al. [27], are written in Hebrew, in a so-called 
squared writing as most characters are made of horizontal and vertical strokes. They are 
reproducing the biblical text of the Pentateuch. Characters are calligraphed by skilled scribes 
with a quill or a calamus. The Scrolls, intended to be used in the synagogue, do not include 
diacritics. Characters and words are written properly separated but digitization make some 
characters touch. Cases of overlapping components occur as characters such as Lamed, Kaf, 
and final letters include ascenders and descenders. Since the majority of characters are 
composed of one connected component, it is more convenient to perform text line 
 20  
segmentation from connected components units. Fig. 17 shows the resulting segmentation 
with the Hough-based method presented in Section 3.5. 
Fig. 17. Text line segmentation of a Hebrew document (Scroll). 
 21  
 Table 1. Text line segmentation methods suitable for historical documents 
Authors Description Line 
Description 
Writing 
Type 
Units Suitable for Project/ 
Documents 
[Antonacopoulos 
and Karatzas, 
2004] 
projection 
profiles 
linear paths Latin 
printed 
pixels separated 
lines 
Memorial/person
al records(World 
War II) 
[Calabretto and 
Bozzi, 1998] 
projection 
profiles (gray 
level image) 
linear paths cursive 
handwriting 
pixels separated 
lines 
Bambi/italian 
manuscripts (16th 
century) 
[Feldbach and 
Tönnies, 2001] 
grouping  
method 
baselines  cursive 
handwriting
minima 
points 
fluctuating 
lines 
Church registers 
(18th, 19th 
century) 
[He and Downton, 
2003] 
projections 
(RXY cuts) 
linear paths Latin 
printed and 
handwriting
pixels separated 
lines 
Viadocs/ Natural 
History Cards 
[Lebourgeois et al., 
2001] 
smearing 
(accumulated 
gradients) 
clusters Latin 
printed  
pixels separated 
lines 
Debora/books 
(16th century) 
[Likforman-Sulem 
and Faure, 1994] 
grouping  strings Latin 
handwriting 
connected 
components
fluctuating 
lines  
Philectre/ 
authorial 
manuscripts 
[Likforman-Sulem 
et al., 1995] 
Hough 
transform, 
(hypothesis-
validation 
scheme) 
strings Latin 
handwriting 
connected 
components
different 
straight line 
directions 
Philectre/ 
authorial 
manuscripts, 
manuscripts of 
the 16th century 
[Oztop et al., 1997] repulsive -
attractive 
network 
baselines Arabic and 
Latin  
handwriting
pixels (gray 
levels) 
fluctuating 
lines (same 
size) 
ancient Ottoman 
documents 
[Pal and Datta, 
2003] 
piecewise 
projections 
piecewise 
linear paths 
Bangla    
handwriting  
segmentatio
n points 
overlapping/ 
touching lines 
Indian 
handwritten 
documents 
[Pu and Shi, 1998] Hough 
transform 
(moving 
window)  
clusters Latin 
handwriting
minima 
points 
fluctuating 
lines 
handwritten 
documents 
[Shapiro et al., 
1993] 
projection 
profiles 
linear paths Latin 
handwriting
pixels skewed 
separated 
lines 
handwritten 
documents 
[Shi and 
Govindaraju, 2004] 
smearing 
(fuzzy run 
length) 
cluster Latin 
handwriting
pixels straight 
touching lines 
Newton, Galileo 
manuscripts 
[Tseng and Lee, 
1999 ] 
stochastic 
(probabilistic 
Viterbi 
algorithm) 
non linear 
paths 
Chinese 
handwriting
pixels overlapping 
lines 
handwritten 
documents 
[Zahour et al., 
2004] 
piecewise 
projection and 
k-means 
clustering 
piecewise 
linear paths 
Arabic 
handwriting
text blocks overlapping/ 
touching 
lines. 
ancient Arabic 
documents        
 22  
4. Discussion and concluding remarks 
An overview of text line segmentation methods developed within different projects is 
presented in Table 1. The achieved taxonomy consists in six major categories. They are listed 
as: projection-based, smearing, grouping, Hough-based, repulsive-attractive network and 
stochastic methods. Most of these methods are able to face some image degradations and 
writing irregularities specific to historical documents, as shown in the last column of Table 1.  
Projection, smearing and Hough-based methods, classically adapted to straight lines and 
easier to implement, had to be completed and enriched by local considerations (piecewise 
projections, clustering in Hough space, use of a moving window, ascender and descender 
skipping), so as to solve some problems including: line proximity, overlapping or even 
touching strokes, fluctuating close lines, shape fragmentation occurrences.  The stochastic 
method (achieved by the Viterbi decision algorithm) is conceptually more robust, but its 
implementation requires great care, particularly the initialization phase. As a matter of fact, 
text-line images are initially divided into mxn grids (each cell being a node), where the values 
of the critical parameters m and n are to be determined according to the estimated average 
stroke width in the images. Representing a text line by one or more baselines (RA method, 
minima point grouping) must be completed by labeling those pixels not connected to, or 
between the extracted baselines. The recurrent nature of the repulsive-attractive method may 
induce cascading detecting errors following a unique false or bad line extraction.  
Projection and Hough-based methods are suitable for clearly separated lines. Projection-based 
methods can cope with few overlapping or touching components, as long text lines smooth 
both noise and overlapping effects. Even in more critical cases, classifying the set of blocks 
into “one line width” blocks and “several lines width” blocks allows the segmentation process 
to get statistical measures so as to segment more surely the “several lines width”  blocks. As a 
result, the linear separator path may cross overlapping components. However, more accurate 
segmentation of the overlapping components can be performed after getting the global or 
piecewise straight separator, by looking closely at the so crossed strokes. The stochastic 
method naturally avoids crossing overlapping components  (if they are not too close): the 
resulting non linear paths turn around obstacles. When lines are very close, grouping methods 
encounter a lot of conflicting configurations. A wrong decision in an early stage of the 
grouping results in errors or incomplete alignments.  In case of touching components, making 
an accurate segmentation requires additional knowledge (compiled in a dictionary of possible 
configurations or represented by logical or fuzzy rules).    
    Concerning text line fluctuations, baseline-based representations seem to fit naturally. 
Methods using straight line-based representations  must be modified as previously to give non 
linear results (by piecewise projections or neighboring considerations in Hough space). The 
more fluctuating the text line, the more refined local criteria must be. Accurate locally 
oriented processing and careful grouping rules make smearing and grouping methods 
convenient. The stochastic methods also seem suited, for they can generate non linear 
segmentation paths to separate overlapping characters, and even more to derive non linear 
cutting paths from touching characters by identifying the shortest paths. 
 23  
Pixel based methods are naturally robust at dealing with writing fragmentation. But, as a 
consequence of writing fragmentation, when units become fragmented, sub-units may be 
located far from the baseline. Spurious characteristic points are then generated, disturbing 
alignment and implying a loss of accuracy, or more, a wrong final representation.  
Quantitative assessment of performance is not generally yielded by the authors of the 
methods; when it is given, this is on a reduced set of documents. As for all segmentation 
methods, ground truth data are harder to obtain than for classification methods. For instance 
the ground truth for the real baseline may be hard to assess. Text line segmentation is often a 
step in the recognition algorithm and the segmentation task is not evaluated in isolation. To 
date, no general study has been carried out to compare the different methods. Text line 
representations differ and methods are generally tuned to a class of documents.  
Analysis of historical document images is a relatively new domain. Text line segmentation 
methods have been developed within several projects which perform transcript mapping, 
authentication, word mapping or word recognition. As the need for recognition and mapping 
of handwritten material increases, text line segmentation will be used more and more. 
Contrary to printed modern documents, a historical document has unique characteristics due 
to style, artistic effect and writer skills. There is no universal segmentation method which can 
fit all these documents. The techniques presented here have been proposed to segment 
particular sets of documents. They can however be generalized to other documents with 
similar characteristics, with parameter tuning that depends on script size, stroke width and 
average spacing.  
The major difficulty consists in obtaining a precise text line, with all descenders and 
ascenders segmented for accessing isolated words. As segmentation and recognition are 
dependent tasks, the exact segmentation of touching pieces of writing may need some 
recognition, or knowledge about the possible touching configurations. Text line segmentation 
algorithms will benefit from automatic extraction of document characteristics leading to an 
easier adaptation to the document under study. 
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