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THE USE OF WORD SHAPE INFORMATION FOR CURSIVE SCRIPT RECOGNITION Robert K. Powalka, Nasser Sherkat, Robert J. Whitrow Department of Computing, The Nottingham Trent University Burton Street, Nottingham NG1 4BU United Kingdom Abstract Word shape information can be helpful in handwriting recognition. It can be used by a segmentation based recognizer to verify the results of the recognition of individual segmented letters. It is often one of the main features used by wholistic recognizers. This paper describes extraction of the word shape information through identification of the middle zone, then ascenders and descenders. Extracted information is applied both in a bottom-up and top-down fashion resulting in a verification mechanism for existing segmentation based system and a new wholistic recognizer. The wholistic recognizer is found relatively weak. However, when the two are combined, a significant improvement is observed. Recognition results are presented. 1. Introduction Zoning information is an important feature in cursive script recognition [8]. It allows distinction between letters which are very similar, either for human readers (e.g. “c” and “C”), or for specific recognition algorithms (e.g. “a” and “d” for an algorithm based on the angular variation of the pen path). It also provides the information about the shape of the word - the number, sequence and position of ascenders and descenders within the word. The information about the word shape considerably limits the number of word alternatives which may represent a handwritten word. It is often possible to identify the word correctly without having to locate and recognize all the letters composing it. This can be an enormous help in the recognition of cursive script handwriting, where illegible or difficult to segment words are rather frequent. It is also speculated that humans do not usually analyse single letters when reading, but rather recognize entire words. This paper describes work on extraction and application of word zoning and shape to cursive script recognition. Two main approaches are described: • bottom-up, where the word is recognized using multiple interactive segmentation [9] and zoning information is used to eliminate many wrong answers; • top-down, where calculated word shape is compared with all words in the lexicon in order to find similar ones. As the word shape recognition on its own is found to produce ambiguous results, recognized letter alternatives are used in order to improve ranking of likely word alternatives. An attempt is also made to merge the best features of both recognizers into a hybrid system. Such a system attempts to recognize all letters within the word, but also looks at the word shape in order to cope with less legible and difficult to recognize words. Methods for extraction and use of the word shape information are presented and the encountered difficulties described. The obtained recognition results are reported and compared with those of a previously developed cursive script recognizer based on multiple interactive segmentation [9].

2. Word shape extraction Throughout this paper the term word shape is understood as the information on presence, sequence and relative position of ascenders and descenders within the word, and the approximate word length. The ascenders and descenders are located using the word zoning information. The word length is estimated using the number of times an imaginary horizontal line intersects the trace of the pen in its most dense area [1]. No estimation of the number of letters within the word is made. Typically two approaches to zoning information extraction are applied [4]: • histogram method; • extrema method. The histogram method is more efficient in detecting the presence of various zones, but has difficulties with reliably locating real boundaries. The extrema method has more difficulties with detecting distinct zones, but is better at locating precise zone boundaries. A combination of the two approaches has been used in order to exploit the advantages of both. The histogram method is used to determine the zoning class of a word. Once the zoning classification is determined, the extrema method is used to decide the zone boundaries. 2.1. Word zoning classification The horizontal word density histogram is analysed for each word. A zone of high density is decided using a threshold derived from the average value of the histogram. The extracted zone is expected to contain most of the middle zone letters. Its width (Width) and vertical position within the word (YPos) are analysed in order to determine the presence of various zones. The process is referred to as word zoning classification (Figure 1a). Fuzzy logic is applied to this task (Figure 1b). Score M YPos Width Width

b) YPos

a)

Fuzzy Logic Inference Engine

Score MU Score ML Score UML

M - middle zone MU - middle/upper zones ML - middle/lower zones UML - upper/middle/lower zones

Figure 1: Word zoning classification: a) choice of decision parameters; b) diagram of the fuzzy logic inference engine.

Figure 2a presents the initial conditions for the word zoning classification. The initial conditions are based on the knowledge about the “ideal” writing. The conditions are experimentally refined to assume the form presented in Figure 2b. This is a normal practice when applying fuzzy logic. The refined conditions for parameter Width differ significantly from the initial ones (Figure 2b). This is due to the fact that letters within the word often do not line up well. This results in a less crisp histogram than for a perfectly written word. Consequently the area of highest density within the word is narrower. The fuzzy inference engine produces individual scores for each possible word zoning classification. A very simple defuzzification function is used (Figure 2c). The highest score indicates the most likely zoning classification. The difference between the highest and second highest score indicates the ambiguity of classification. No attempt is made to produce a single answer. Effectively, the defuzzification only rescales the results of fuzzy AND of the initial conditions (Figure 2c). A more classical defuzzification process, which would merge the four obtained scores into one, is less straightforward. This is because each possible zoning class can be confused with two others,

resulting in a circular relationship (Figure 2d). The defuzzification would have to be performed in such a circular fashion. This could be achieved, however it would not change the interpretation of the results. degree of membership

YPos (YPos ≈ 50%) ∧ (Width ≈ 100%) ⇒ M (YPos ≈ 25%) ∧ (Width ≈ 50%) ⇒ MU (YPos ≈ 75%) ∧ (Width ≈ 50%) ⇒ ML (YPos ≈ 50%) ∧ (Width ≈ 33%) ⇒ UML

1

b) Score

M ML

MU UML

d)

c)

fuzzy AND of conditions

M - middle zone MU - middle/upper zones ML - middle/lower zones UML - upper/middle/lower zones

degree of membership

Width

ML

% of word length

0

a)

M, UML

MU

1

0 1

0

25 UML

75

50 MU, ML

100

M

% of word length

20

100

65

40

score [%]

100

50

Figure 2: Fuzzy word zoning classification rules: a) ideal; b) experimental; c) defuzzification function; d) neighbouring zoning classes.

Table 1 presents the results of word zoning classification of 3600 handwritten words. It can be seen that nearly 20% of words were not correctly classified. This rate is similar to results reported by other researchers [4]. However, when considering the two best choices of the word zoning classifier, the majority of words are classified correctly. Position of the correct classification

Total

Cumulative

1. 2. 3.

81.2% 16.2% 2.2%

81.2% 97.4% 99.6%

Table 1: Effectiveness of word zoning classification.

2.2. Location of ascenders and descenders The obtained zoning classification is used as a guide for locating ascenders and descenders. They are located using local extrema. A simple threshold method is used to decide whether particular extrema are ascenders/descenders or belong to the middle zone (Figure 3). Ymax Tasc = ave (Ymax,HYmax)

Y ≥ Tasc ⇒ Ascender

Tdesc < Y < Tasc ⇒ Middle Zone

HYmax

Y ≤ Tdesc ⇒ Descender

HYmin Tdesc = ave (Ymin,HYmin) Ymin

Figure 3: Location of ascenders and descenders.

Currently, threshold lines are always horizontal. This may cause problems for some handwriting styles, where letter size is inconsistent. Non-horizontal threshold lines need to

be considered. Allowing the threshold lines to be tiered, like in [12] would further increase the flexibility. Word zoning classification choice

Total

Cumulative

Best Second best Other

65.6% 7.4% 1.9%

65.6% 73.0% 74.9%

Table 2: Effectiveness of ascenders/descenders location.

Table 2 presents the effectiveness of locating the ascenders and descenders for different choices of word zoning classification. It can be observed that the obtained results are lower than those of word zoning classification (Table 1). This indicates problems with the method chosen. However, it has to be remembered that the test did not take into account any individual variations of handwriting style (e.g. middle versus middle/lower zone “z,” upper/ middle/lower versus middle/upper zone “f” and others, like “r” with an ascender). Also, not all “i” dots and “t” crosses were eliminated successfully. Other clustering methods, based on vertical proximity of the local extrema points, were applied and provided similar results. 3. Bottom-up application of shape information This section describes the bottom-up application of zoning information. A segmentation based cursive script recognizer is used. Letters are segmented and recognized using multiple interactive segmentation [9]. A lexicon is applied to discard disallowed letter sequences. A number of feature filters are applied to each obtained word alternative in order to verify its recognition score [10]. These include a measure of the amount of ink data representing the letters, horizontal overlaps, dots and dashes, and a very approximate zoning [8]. More reliable zoning information could be used to replace zoning related feature filters. It could be applied before the lexical look-up and considerably reduce the graph of letter alternatives. Knowing the zoning classification of the word, the recognition proceeds in the context of the calculated zoning information. This is referred to as zoning context. Letter graphs for all possible word zoning classifications are created and analysed within their particular zoning contexts. 3.1. Letter zoning classification Letter zoning classification is performed in respect of the chosen word zoning class. The simplest approach is to calculate zoning lines and reject all the letter alternatives not conforming to them. This would however make the correct recognition impossible when calculated zoning lines are wrong. A fuzzy logic based method of letter zoning classification has been introduced. Two versions of the letter zoning classifier have been developed using the following parameters: • letter height and vertical position of the middle of the letter within the word; • positions of vertical extrema points of the letter. The parameters are used in a fuzzy zoning inference engine similar to the one used for the word zoning classification (see Section 2.1, Figure 2b) Neither method performs better zoning classification than the threshold method used for calculating zoning lines (see Section 2.2). Their significant advantage is that they produce “soft” results, returning scores for each possible classification. When a letter is on the boundary of two zoning classes, scores obtained for both classes are of similar magnitude. This is an indication that the particular letter is an ambiguous case and should be dealt with accordingly. In effect, the same data can be allowed in more than one letter zoning class,

improving the ability of the system to deal with unclear cases. Distinction between clear and unclear cases is based upon the ratio of the best and second best zoning scores. If the ratio is higher than a threshold, the case is considered clear and only one zoning class is specified. The threshold is currently empirically chosen and set to 3.0. Letters “z” and “f” are always allowed to belong to two zoning classes. 3.2. Use of recognition context The bottom-up application of zoning is performed in word zoning contexts (Figure 4). Letters are first segmented and recognized. Further, within each context, letter alternatives are pruned using fuzzy logic based letter zoning classification (see Section 3.1). Obtained from this process smaller letter graphs are subjected to lexical analysis. Scores obtained in word zoning classification (see Section 2.1) are assigned to each zoning context. Confidences of word alternatives obtained within each context are influenced by the context score. Word alternatives obtained in all word zoning contexts are collected together and sorted according to their confidence. middle zone context

middle/upper zone context

middle/lower zone context

upper/middle/lower zone context

Figure 4: The use of recognition zoning contexts.

Currently, all four zoning contexts are considered. Table 1 indicates that considering other than the two highest scored zoning contexts could improve the results only slightly. However, as the complexity of processing the letter graph grows exponentially with its size, processing of two small extra graphs does not impose a large overhead. 4. Top-down application of shape information Zoning information allows to encode the shape of the word in terms of the sequence and position of ascenders and descenders. This coding is used to recognize whole words, without attempting to segment them or recognize letters they consist of. Such an approach has the potential of dealing with writing which is illegible or difficult to recognize. The word shape on its own was found insufficient to identify the correct word within larger dictionaries. For this reason attempts to use letter recognition in order to improve the word shape recognition have been made. A resulting recognizer benefits from letter segmentation and recognition of the multiple interactive segmentation method, but is not limited to legible words. It uses word shape to “guess” parts of words which it could not recognize. 4.1. Word shape matching The word shape matching is performed in two stages: • matching the sequence of ascenders/descenders obtained from zoning information (Figure 5a);

• assessing the similarity of the unknown pattern to all the patterns with identical sequence of ascenders/descenders (Figure 5b). The matching of the sequence of ascenders/descenders is performed in order to limit the number of words to be considered in greater detail. It is performed in a strict manner. A database of word coding is created using information about the “ideal” shape of perfectly written words. Each letter has certain shape attributes associated with it. Word shape is obtained by concatenating shape attributes of letters composing the word. Once words with identical sequence of ascenders/descenders have been identified, it is necessary to choose the one most similar to the unknown word. To this purpose a degree of similarity is assessed for each of the identified words. The word comparison takes into account the position of ascenders/descenders within the word. In order to compare different words, their lengths are normalised to be equal. Fuzzy logic is applied to the word shape comparison [14]. Positions of ascenders/ descenders within the matched word are represented as fuzzy sets (Figure 5b). For each of the sets the degree of membership of the position of appropriate ascender/descender within the database is calculated. The final score is calculated using the following formula: Score = min ( F 1 ( X 1 ) , F 2 ( X 2 ) , ... , F n ( X n ) )

(EQ 1)

where Score is the final similarity score in percent, n is the number of ascenders/descenders, Fi is a degree of membership of the position of i-th ascender/descender of the database pattern within the fuzzy set constructed around the i-th ascender/descender of the unknown pattern, and Xi is the position of the i-th ascender/descender in the database pattern. The minimum function implements AND in fuzzy logic [14]. This implies that all the ascenders/descenders within the compared words should be in their expected positions in order to obtain a good similarity score. Word length is further taken into account in order to distinguish between words of similar shape but differing in length. The criterion is particularly important for words with no ascenders/descenders. a)

b)

F1

middle zone

x1 x0 x1

x2 x3 x4 word length

X1

F3

F2

data pattern

x2 x3 X2

X3

database pattern

Figure 5: Word shape matching: a) deriving word shape using zoning information; b) calculating similarity of the data and database patterns.

4.2. Use of recognized letter alternatives Word shape encoding applied in the described system ignores nearly all information that can be found in the middle zone. Features like loops, arcs, cusps, concavities and convexities (e.g. a set of features described in [3]) could be used to make word encoding more specific. Another possibility is to attempt to locate and recognize all the letters within the word and

treat them as compound features. The multiple interactive segmentation [9] is a readily available mechanism for location and recognition of letters within a word. The number and confidence scores of located letters are used to judge the degree of similarity of the proposed word to the data being recognized. For each word alternative proposed by the word shape matching, the letter graph can be searched for the best combination of letters composing the proposed word. Any number of letters are allowed to be missing from the path through the letter graph. The letter graph obtained from the multiple interactive segmentation is pruned using the zoning information. As zoning is implicitly applied in a strict manner by the word shape encoding, letters which do not conform to it are simply rejected. No relaxed treatment is necessary here. When analysing the letter graph, the position at which each letter is allowed is limited. This is to avoid possible high scores for any approximate substrings of the word being matched. Limiting the letter position also avoids opposite cases, the unwanted insertion of a number of letters that are not recognized. As the letter verification allows any number of letter alternatives to be missing, insertion of letters within a confined space where it is impossible for them to be located would produce spurious results. Scores obtained from word shape matching and letter verification are averaged to produce the final score. 5. Hybrid system The recognition approaches presented in Sections 3 and 4 have both certain advantages and disadvantages. The multiple interactive segmentation based recognizer using the zoning information in a bottom-up manner always locates and recognises single letters. Hence it is expected to produce more reliable results. No time is spent searching the lexicon or verifying all the word alternatives with similar shapes. However, this method will fail on words in which individual letters cannot be located and/or recognized. The top-down application of the word shape, on the other hand, is expected to cope with less clearly written words. The success of the word shape recognition depends on the extraction of the zoning information. Also, as the lexicon size grows, several words can be found to have very similar shapes. As a result, the word shape recognition has difficulty in choosing the correct answer as the best alternative, even though it is likely to find the correct word more often. In order to maximise advantages of both approaches, they are combined into a hybrid system [10]. Both multiple interactive segmentation and word shape letter recognizers are applied and their results are combined. The objective is to produce the correct word alternative even for difficult to recognize words and to increase its ranking proportional to the number of letters that can be identified and recognized. When the recognition process is complete, the two sets of word alternatives are merged and the results are sorted according to their confidence scores. If a particular word alternative is proposed by both recognizers, its confidence is increased proportional to the higher one. The results combination is biased towards the multiple interactive segmentation based recognizer. The biasing factor has been arrived at experimentally. 6. Results Tests were performed with eighteen writers writing 200 words each. The test words were one to sixteen letters long. An NCR 3125 pen computer was used. Writers were instructed to write legibly. Sixteen writers are right-handed, two are left-handed. Thirteen writers have had no previous experience with the “electronic paper.” A medium size lexicon of 4107 words was used. No specific criteria for selecting words of the lexicon were used.

Two sets of results are observed: • recognition rate when only the best word alternative returned by the recognizer is taken into account; • recognition rate when five highest ranked word alternatives returned by the recognizer are taken into account. The top word alternative is the actual measure of performance. This is considered as the most important result. However, considering a number of word alternatives returned by the recognizer is useful for better understanding of the obtained results. Also, when higher level contextual processing is applied, it will use a number of word alternatives for each recognized word [6][11]. Number of alternatives considered

Recognizer SEG

SEGZN

SHAPE

SHLTR

HYBRID1

HYBRID2

1 5

48.5 / 22.0 54.2 / 23.1

46.0 / 23.1 50.3 / 23.9

13.9 / 8.5 30.4 / 13.9

39.1 / 17.4 53.8 / 19.8

53.0 / 20.8 65.8 / 19.5

60.5 / 19.0 72.8 / 17.5

Table 3: Average recognition rates for various tested recognizers (mean recognition rate [%] / standard deviation).

Results of six recognizers are presented in Table 3: • multiple interactive segmentation recognizer (column SEG), described in [9][10]. Results are provided for comparison only; • multiple interactive segmentation recognizer with bottom-up use of the zoning information (column SEGZN); • word shape recognizer (column SHAPE); • word shape recognizer with letter verification (column SHLTR); • hybrid recognizer SEG x SHAPE (column HYBRID1); • hybrid recognizer SEG x SHLTR (column HYBRID2). As shown, the bottom-up application of the zoning information does not increase the recognition rate in comparison to the recognizer using zoning-related feature filters [8]. 0 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 1151 (32.0%) 1 xxxxxxxxxxxxxxxxxxxxxxxxx 905 (25.1%) 2 xxxxxxxxx 326 ( 9.1%) 3 xxxxxx 220 ( 6.1%) 4 xxxxx 162 ( 4.5%) 5 xxxxxxxxxxxxxxxxxxxxxxx 836 (23.2%)

a)

ave = 1.96

0 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 1510 (41.9%) 1 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 1092 (30.3%) 2 xxxxxxxxxx 356 ( 9.9%) 3 xxxxx 170 ( 4.7%) 4 xxxx 137 ( 3.8%) 5 xxxxxxxxx 335 ( 9.3%)

b)

ave = 1.26

Figure 6: Histograms of word alternatives produced by the recognizers: a) recognizer SEG; b) recognizer SEGZN. Numbers in the first column indicate the number of word alternatives. Zero alternatives means rejection.

However, the recognizer becomes more “certain.” The rejection rate rises and the average number of word alternatives produced per each processed word decreases (Figure 6). This can be considered an advantage. The recognition time is also decreased by the bottom-up use of the word shape information. The top-down application of the word shape information produces low recognition rates. The results improve when top five word alternatives are taken into consideration. For some data sets, difficult for the multiple interactive segmentation based recognizer, the results of word shape recognizer are better than those of the multiple interactive segmentation. The use of letter alternatives to verify the results of the word shape recognizer considerably improves the recognition rate. The average recognition rate for top five word alternatives is actually better than for the multiple interactive segmentation based recognizers. For some data sets the improvement is very significant.

The hybrid system presents a significant improvement over all the other recognizers. The system successfully merges best features of SEG and SHLTR providing an improvement of around 12% in average recognition rate for the best word alternative and around 19% for top five word alternatives. The trade-off is the increased processing time and decreased “certainty” of the system, as the word shape recognizer nearly always produces some answers. Limiting the lexicon size usually has a positive effect on the recognition rate [10]. In the case of word shape and hybrid recognizers, the improvement is significant. Table 4 presents average recognition rates obtained by different recognizers for a lexicon of 200 words. Number of alternatives considered

Recognizer SEG

SEGZN

SHAPE

SHLTR

HYBRID1

HYBRID2

1 5

53.7 / 22.9 54.6 / 23.1

49.9 / 23.9 50.6 / 24.0

40.8 / 16.3 60.6 / 20.3

57.0 / 19.6 63.4 / 20.6

70.0 / 18.0 78.8 / 15.8

74.8 / 16.3 80.1 / 15.1

Table 4: Average recognition rates for various tested recognizers, small lexicon (mean recognition rate [%] / standard deviation).

The word shape recognizer with letter verification can be used on its own. It outperforms the multiple interactive segmentation based recognizers. The hybrid recognizer provides yet better recognition rate. 7. Discussion The results obtained so far are encouraging. The system was tested with a usable size lexicon and a significant number of writers, including many unknown to it. The word shape templates used are constructed using a priori knowledge about writing. No additional training is performed. Fuzzy logic is used to deal with the handwriting variability. The word shape recognition is limited by word shape calculation capability. It is extremely difficult to extract reliable zoning information for some handwriting styles. Such styles are currently not isolated and consequently treated like all other cases. A better approach might be to identify handwriting styles difficult to zone and lower the influence of the word shape recognizer [7]. Some individual handwriting variations strongly affect the word shape. The system currently lacks any mechanisms to adjust to such variations. The word shape recognizer, contrary to those described in [1][2], uses very few features. Hence it has problems with disambiguating the results. It appears more suitable for limiting the recognition domain, than the actual recognition. The position of the recognized letter alternatives used for verification is limited within the word. Approximate metrics of letter width are used. The position limits vary for each database word pattern that the data are compared to. This produced better results than estimating the number of letters and dividing the word into equal regions, as presented in [1]. Highest results are obtained for the hybrid system. In this case the word shape recognition with letter verification appears to be the natural extension to the word ending postulation [10]. Attempts to use word shape in conjunction with other approaches are reported also for static recognition, e.g. [5]. The currently used results combination approach is very simple. More sophisticated approaches require a statistical assessment of the recognizer characteristics [13]. 8. Further work Further work concentrates on two main areas: methods for results combination and reducing the current limitations of word shape calculation accuracy. More sophisticated methods for results combination are being investigated. More than two recognizers can be used together. Handwriting style assessment can be used to automatically

choose the best recognizers for a particular handwriting style as proposed in [7]. The more strict recognizer, based on the multiple interactive segmentation, can be used to adjust the word shape recognizer to a particular style. Methods for assessment of whole word recognition characteristics are investigated to provide guidance for results combination. The word shape calculation can be improved further. Other features, like dots and dashes, can be taken into account. Better methods for locating ascenders and descenders are needed to improve the word shape matching results. In addition, experiments show that considering ascenders and descenders separately improves results, particularly for small lexicons. Work is in progress on a word shape matching method that is not limited by the accuracy of the zoning information extraction. Obtained average results reach 48% and 66% for the first and top five alternatives, respectively.

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