Towards Syriac Digital Corpora

The analysts sought to discover which were the most predominate kinds of errors, which combinations of type styles and Tesseract modes were most accurate for which kinds of letters, and what were the overall accuracy rates scholars could expect with the current version of Tesseract. The project further sought to identify important areas for future training of the Tesseract engine. Analysis began with the most detailed descriptions of errors possible and grouped error types into increasingly broader categories. At the most narrow level, the analysts considered categories such as ayn becoming ḥéth in East Syriac-Script, which only occurred once across all three pages. At the broadest level, the test tabulated total percentage of consonant errors compared across all six combinations of type style and Tesseract mode. The team also attempted to evaluate the impact of other factors such as pixel size and image quality on the OCR results. The section that follows details the specific results for each type style, subdivided into discussions of consonants, diacritics, punctuation, and non-Syriac characters.

In the broadest terms, Tesseract generated the most accurate results with the Estrangela type style and using Tesseract’s Language mode. The East Syriac type style paired with Script mode produced the lowest accuracy rates overall. Unsurprisingly, diacritics caused the most errors across all type styles and modes, while consonants were more easily recognized and differentiated. Punctuation and other non-Syriac characters also caused some errors across the board.

Figure 5: Overall Accuracy Rates

The analysts, each of whom examined different pages, naturally recorded different OCR errors.40 The tested pages generated various kinds of errors, some of which were repeated across other pages and some of which were unique to one page—and sometimes unique to one instance. Though the analysts made great effort to be diligent and consistent in their individual work and as a team, human error must be acknowledged as a reality in any project that relies on human eyes. That being said, the pages studied and the resulting data were discussed and reexamined multiple times to give the team confidence in the data presented here.41 The analysts were also randomly assigned to review different variables from the other analysts’ data.

The individual data was analyzed to determine the range of the data set as well as the variety of accuracy rates within the pages. Most of the data given in this paper is based on the averages of the three analysts’ work, as this gives the best overall picture of the results and compensates for human error. However, the range and standard deviation between individual data gave the team a deeper understanding of the underlying data points, and these are provided in tables in Appendix 2. For the most part, the data remained reasonably close between analysts; consonant accuracy rates only have ranges of one to four percent, while the ranges of total accuracy rates do not reach six percent. Diacritic accuracy rates, on the other hand, vary greatly between individual pages. For example, the ranges within the data for the Estrangela and Serto texts in Script mode hovered around 55%.42

This section gives a general numerical summary of the test results. Tables and graphs, rather than prose, dominate this section in order to communicate clearly the most crucial pieces of data. The results of each type style are given, along with brief discussions about consonants, diacritics, punctuation, and non-Syriac characters. Further data can be found in Appendix 2.

Estrangela consonants were recognized very accurately with 99.28% of consonants OCRed correctly in Language mode and 98.51% OCRed correctly in Script mode. A total of 22 different types of consonantal error were recorded, 16 of which occurred only once or twice. This low number of occurrences for the majority of errors makes it difficult to determine their cause.

Table 4 shows the six consonantal errors which occurred three times or more. The most frequent error was word truncation, which caused three consonants to be deleted in Language mode and eight to be deleted in Script mode. The second most common error was a yudh being inserted into the text, which occurred twice in Language and six times in Script. These two errors were also common in Serto and East Syriac, and they are discussed in more detail in the Analysis.

Table 5 gives the individual accuracy rate for each consonant in the two modes. In both modes, the majority of consonants were recognized with 100% accuracy, and only one consonant in each fell below 97% accuracy.

The Estrangela pages chosen for testing were all unvocalized, meaning that in terms of diacritics they contained only homograph dots and syomés. In Language mode, only 45.45% of these diacritics were recognized correctly, and in Script mode, only 32.17% were recognized correctly. The most frequently occurring error was the diacritic homograph dot being substituted for another kind of diacritic. It was most often rendered as one of the very similar-looking dotted vowels, but was also occasionally misidentified as a Greek vowel, an Arabic diacritic, or a syomé. The second most common error was the homograph dot being missed or added, which happened 14 times in Language and 32 times in Script.

There were 78 Syriac punctuation marks on the Estrangela pages tested, with 12 errors recorded in Language mode and 14 recorded in Script. Only two types of errors were noted. The most common was the addition or omission of punctuation, which happened 12 times in Language and 11 times in Script. There were also three occurrences where a Syriac punctuation mark was recognized as an Arabic diacritic—for example, the pāsūqā was recognized as a sukun—and this error was exclusive to Script.46 Syriac punctuation was only substituted with Arabic characters in Script mode, but diacritics were recognized as Arabic characters in both Language and Script modes.

There were 21 non-Syriac characters in the Estrangela pages tested, consisting of asterisks and superscript numbers. None of these characters was recognized accurately by Tesseract, and the errors involving these non-Syriac characters were very inconsistent. For example, on one page there were ten superscripted Arabic digits (i.e., 1, 2, 3) identifying footnotes.47 Nine of these numbers were rendered as other characters—one as an asterisk, three as double quotation marks, four as single apostrophes, and one as an exclamation point.48 Only one numeral was recognized correctly, but Tesseract did not recognize that it was superscripted and instead placed it on the main line of text. Interestingly, two asterisks were rendered as double quotation marks, despite Tesseract evidently having an asterisk in its training module. Running English Language or Latin Script modes at the same time as Syriac did not improve the recognition of these characters, only resulting in an increased number of Syriac characters recognized as Latin characters (e.g., Estrangela hé recognized as Latin m).

Attentive readers may be perplexed by the negative percentages in this category: they are caused by Tesseract’s addition of non-Syriac characters which were not present in the original images. This occurred in Script mode on only two occasions, but these two additions had a notable effect on the accuracy rate because of the small number of non-Syriac characters on the original pages.

In Language mode, Tesseract recognized 93.67% of consonants accurately. In Script mode, 88.98% of consonants were recognized accurately. The consonantal errors identified in Serto varied widely, with over 60 different types of errors recorded. Half of these errors only occurred once or twice—again making it difficult to identify any patterns. However there were also certain errors which occurred with a particularly high frequency. Table 9 lists the 15 most commonly occurring errors and gives their frequency in both Language and Script mode.

The most common issue in Serto was the addition of extra letters, particularly the tooth letters yudh, ayn, and ḥéth. This was common in both Language and Script mode but occurred twice as many times in Script mode. This is discussed in more detail in the Analysis section under Added Tooth Letters. Dolath-rish, waw, olaph, and béth were also incorrectly added to the text fairly frequently. Waw and olaph were inserted more times in Script mode, while dolath-rish was inserted more often in Language mode, and béth was inserted an almost equal number of times in both modes.

After the addition of consonants, misidentification of consonants was the second most common cause of errors. Certain consonants, when misidentified, were misidentified consistently. For instance, when Tesseract misidentified a Ṣodhé, it was always substituted with a dolath. Similarly, on all the occasions that taw was misidentified, it was recognized as an olaph. Other consonantal substitutions were not completely consistent but still showed a tendency towards a particular letter. For example, shin was most likely to be misidentified as phé, which accounted for seven out of the nine substitution errors in Language and both of the substitution errors in Script. Hé was most frequently misidentified as a waw in both modes, and in Script it was also frequently mistaken for a zayn and waw together, which in Serto type style creates a very similar shape to hé. Yudh was mistaken for a ḥéth 11 out of the 16 times it was misidentified and, likewise, all nine times ḥéth was misidentified it was mistaken for one or two yudhs. The remaining two consonants that were frequently misidentified, mim and dolath-rish, showed more variation in their substitutions, with the 13 mim substitutions occurring across ten distinct error categories and the ten dolath-rish substitutions occurring in eight distinct error categories.

Table 10 shows the accuracy rate of each consonant in Serto in both Language and Script modes. The majority of consonants achieved over 97% accuracy in both Serto modes. However, when compared with Estrangela, Serto's numbers become less praiseworthy. Serto has an average of eight consonants performing at lower-than-97% accuracy, while Estrangela only has one that falls below this threshold. Moreover, certain consonants in Serto had a significantly low accuracy rate. Ṣodhé, for example, proved particularly difficult for Tesseract to recognize. Also illustrated by the table is the fact that the two modes can produce extremely varied accuracy ratings for the same letter. For example, ḥéth, taw, and mim were over 10% more accurate in Language mode, whereas shin was 19% more accurate in Script mode.

As with Estrangela, the Serto pages tested were unvocalized, meaning that the images’ only diacritics were homograph dots and syomés. Of all three type styles tested, Tesseract produced the least accurate results for diacritics in Serto, with only a 22.52% accuracy rate for diacritics in Language mode and a 16.56% accuracy rate for diacritics in Script mode. These figures include a high number of diacritics that were incorrectly added by Tesseract, many of which the analysts believe were caused by stray marks in the original images.

Table 11 lists the seven different types of diacritic errors recorded for Serto and gives their frequency in Language mode and Script mode. The homograph dot was the locus of most Serto diacritic errors; it was frequently added, deleted, or substituted. When the homograph dot was misidentified it was most often substituted for a dotted vowel, as was also the case in Estrangela.

There were 74 punctuation marks on the Serto pages tested, and 15 errors were recorded in Language mode and 29 recorded in Script mode. The most common punctuation errors were the addition or omission of punctuation, accounting for about 73% of punctuation errors in both Language and Script. The remaining errors involved punctuation being misidentified—usually as another Syriac punctuation mark, though in one instance, Serto-Script rendered a punctuation mark as an Arabic diacritic.

The Serto pages tested contained only three non-Syriac characters, consisting of asterisks and one superscript numeral. None of these were recognized correctly by Tesseract. Again, a minor number of added non-Syriac characters caused the negative accuracy rates—one extra non-Syriac character was inserted in Language mode and two were inserted in Script mode.

Tesseract recognized 88.67% of consonants accurately with Language mode and 88.16% with Script mode. The consonantal errors were incredibly varied, with 99 different types of consonantal error recorded. Figure 6 gives a visual representation of the distribution of these errors.

Figure 6: Distribution of Consonantal Errors in East Syriac

Since so many different categories of consonantal errors were produced in East Syriac and the majority of errors only occurred once or twice, it is difficult to discuss them all in depth and to discern patterns within the data. Table 14 lists the 12 most frequently-occurring errors and gives their frequency in both Language and Script modes. Here, specific errors have been subsumed under more general categories in order to provide a broader picture of the errors encountered.

The issue that most affected Tesseract’s accuracy with East Syriac text in both modes was word truncation, a problem resulting from segmentation issues. This caused Tesseract to omit 80 consonants in Language and 82 in Script. Tesseract also omitted many individual consonants from within words that had otherwise been recognized. The most commonly omitted consonants were olaph, dolath-rish, nun, and ḥéth.

The majority of misidentification errors related to dolath-rish. Not only were these the most commonly misidentified consonants, they were also the consonants that others were most frequently substituted for. This issue is discussed in more detail in the Analysis section. Another frequently substituted consonant was ṭéth, which was substituted for a gomal on every occasion that it was misidentified.

Table 15 gives the accuracy rate for each individual consonant in East Syriac. Tesseract was less than 97% accurate at recognizing the majority of consonants in both modes. The letters dolath-rish, ṭéth, and zayn posed particular difficulty for Tesseract in East Syriac type style. The table also illustrates some of the large differences in accuracy of individual consonants between the two modes. Zayn is 50% more accurate in Language and béth is 11% more accurate in Language, while Ṣodhé is 20% more accurate in Script.

The original East Syriac pages tested contained syomés, homograph dots, dotted vowels, and Syriac oblique lines (Unicode character U+0747). Tesseract recognized the diacritics in East Syriac most accurately out of the three type styles tested, but the accuracy rate was still very low, with a 60.69% rate of accuracy in Language and a 53.17% rate of accuracy in Script.

Both the relative accuracy and the (still) high number of errors in East Syriac are likely due to the high frequency of vowel pointings in East Syriac to begin with. The three pages tested had a total of 1,239 original diacritics. The more diacritics there are and the more kinds of diacritics there are, the more opportunities Tesseract has to misread the image.51 By the same token, because East Syriac has so many more diacritics than the other type styles, Tesseract has far more chances to read them correctly. Tesseract can have 487 errors and 579 diacritical errors in East Syriac-Language and -Script, respectively, yet still achieve the highest percentage of diacritic accuracy of all three type styles tested.

Tesseract’s two most frequent diacritic errors involved the dotted vowel. Dotted vowels (e.g., ܲ and ܵ ) were likely to be deleted or added, and they were also occasionally rendered as Greek vowels (e.g., ܰ and ܶ). When this switch from dotted vowel to Greek vowel occurred, the dotted vowels were usually rendered as the equivalent Greek vowel and were rendered as a non-equivalent Greek vowel roughly half as many times.52 This was true for both modes. This indicates that Tesseract has at least some training in recognizing that East Syriac dotted vowels are, in fact, vowels and in distinguishing them from punctuation marks or homograph dots. This also implies that Tesseract was trained on texts that included Greek vowels.

There were 80 punctuation marks in the East Syriac pages tested. The primary issue recorded was punctuation being added or deleted, accounting for over half of total punctuation errors. The remaining errors consisted of punctuation being misidentified—punctuation marks were most often substituted with other Syriac characters but on anomalous occasions were also recognized as Arabic or Latin characters.

There were no non-Syriac letters, numerals, or symbols in the original East Syriac pages, and so an accuracy rate was not calculated for this category.

With the detailed results for each type style fully recounted, this paper turns now to an analysis of prominent and unusual error trends that were observed during testing. To put it in terms of the popular metaphor, now that the individual trees have been catalogued, the following section will describe the forest.

Figure 8: Tooth Letter Errors as Percentage of Consonantal Error

The kinds of tooth letter errors varied between the different type styles. By far the most common type of tooth letter error in Serto was added tooth letters. East Syriac, on the other hand, had more substitution errors, in which one tooth letter was misread as a different tooth letter, and errors of tooth letter deletion. The types of tooth letter errors were relatively even in Estrangela, with four, eight, and six total substitution, addition, and deletion errors, respectively.

As Table 20 shows, Tesseract rendered tooth letters with a range of accuracies across the six combinations of type style and mode. Both Estrangela modes had higher than 97% accuracy rates at rendering all four tooth letters, and the two East Syriac modes ranged from 90.61% to 93.01% accurate. Particularly for Estrangela, Tesseract may be relied upon with a high degree of certainty in regard to these four letters (ḥéth, yud, nun, and ayn). Serto, on the other hand, performed relatively poorly. Serto-Language generated an 82.96% accuracy rate, and Serto-Script was only 68.17% accurate.

Figure 9: Tooth Letter Errors as Percentage of All Tooth Letters

Zayns are less similar to the “tooth letters,” differentiated particularly by lack of script attachment to surrounding letters. However, when a zayn precedes a medial nun or yudh Tesseract occasionally recognized the two letters as a ḥéth. This happened once each way in East Syriac-Script.

As illustrated by Table 21, the three type styles varied in their encounters with added tooth letter errors. Surprisingly, East Syriac had the least problems with added tooth letters, and East Syriac-Language did not add any tooth letters. The OCR process only added a few tooth letters in Estrangela-Language and Estrangela-Script. In fact, Estrangela’s only tooth letter errors were eight yudhs added across all six pages (two in Language and six in Script).

Contributing in large measure to Serto’s poor numbers regarding tooth letters, is the frequent addition of yudhs in both Serto-Language and Serto-Script modes. While the reasoning behind this phenomenon is unclear, Tesseract added many yudhs during the OCR process. Sometimes—though not every time—these appeared to be added when a dot rested above the line of text in the original image, as if Tesseract believed a letter was pushed out of line. The differences between Serto and the other type styles are stark: while East Syriac added only one yudh across all six pages tested (the one occurrence was in Script mode) and Estrangela added eight yudhs, Serto inserted an astounding 41 yudhs in Language and 84 in Script! This accounts for about 30–35% of consonantal errors in both of Serto’s modes. Consequently, added yudhs also account for a large percentage of added tooth letter errors in Estrangela and Serto.

In Serto-Language and Serto-Script, however, 48 and 111 tooth letters were added, respectively. Most of its added tooth letters were yudhs: Serto’s many added yudhs made up 85.42% of added tooth letter errors in Language mode and 75.68% in Script mode. If yudhs are discounted, Serto only had 7 tooth letter additions in Language and 27 in Script.

Another unique error within Serto came when one line in both Serto-Language and Serto-Script added multiple ḥéths that could not be accounted for with stray marks.58 This error only occurred one time elsewhere on the page. The ḥéths were added when there was a longer connecting line between two letters, such as a yudh and a taw. The rest of the page did not see such a wide expanse between letters, and it is possible that Tesseract added the ḥéths to make up for the added space between letters.

Given the similar appearances of each tooth letter to another, the analysts anticipated prior to the testing that a high rate of substitution errors would result, wherein one tooth letter (an ayn, for instance) would be incorrectly recognized as a difference tooth letter (say, a nun) by Tesseract. In actuality, Tesseract had a very high accuracy rate in this regard. The least-accurate permutation tested, Serto-Language, still only misidentified 2.01% of tooth letters as other tooth letters. Put another way, while added and deleted tooth letters made up a high percentage of consonantal errors, confusion between letters did not significantly contribute to the error rate. Tesseract thus appears to have a high degree of accuracy in recognizing tooth letters, and evidently other factors cause the added letters.

While close to 100% of dolaths and rishes in Estrangela and over 92% in Serto were recognized accurately in both modes, East Syriac had relatively imprecise accuracy rates for these letters: 72.94% in Language and 75.23% in Script. Close to 25% of dolaths and rishes were incorrectly identified or deleted in each East Syriac mode. This may have to do with the shape of rishes and dolaths in the East Syriac font. With their large curved shape, they look similar to waws and kophs. Indeed, Tesseract substituted a total of 35 dolaths and rishes for waws and a total of 14 dolaths and rishes for kophs. Interestingly, the misidentification did not go both ways; on no occasion was a waw substituted for a dolath or rish, and kophs were very rarely identified as a rish or dolath (only three total occurrences of this type of error in both East Syriac modes). Tesseract also had some difficulty distinguishing dolaths and rishes from each other and from the dotless dolath-rish consonantal stem. These were incorrectly substituted amongst themselves 18 times in East Syriac-Language and 11 times in East Syriac-Script.

Figure 10: Frequency of Consonants Substituted for Dolath-Rish in East Syriac

The East Syriac dolath-rish also proved problematic for Tesseract in another way. Not only did Tesseract often misidentify dolath-rish as other consonants, it also frequently mistook other consonants for a dolath or rish. After dolath-rish, the most commonly misidentified consonants in East Syriac were nun, zayn, olaph, and shin, all of which were most frequently mistaken as dolath-rish. The affected nuns were all medial except one. Perhaps the similar height of the medial nun compared with the dolath-rish stem caused this issue. Zayn and shin are also similar in height and shape to the dolath-rish stem. Olaph stands out as an unusual consonant to be mistaken as dolath-rish, as it is not similar in height or shape. It is possible that East Syriac’s dotted vowels had an effect in these cases. In Script mode, four out of the five instances in which olaph was recognized as a dolath occurred when dotted vowels appeared below the original olaphs. This indicates that Tesseract’s ability to accurately recognize East Syriac text could be improved by further training focused on distinguishing dolath-rish from consonants with similar shapes and further training to distinguish diacritics from dolath-rish dots.

Mims exhibited a similar pattern, being very accurately rendered in two type styles but less-accurately so in the third. Estrangela had a nearly 100% accuracy rate in both modes, and East Syriac-Language and -Script were both about 98% accurate. However, Serto-Language was 95.8% accurate, and Serto-Script was 86.55% accurate. The different shape of mims in Serto likely contributes to Tesseract’s less-accurate performance here. A glance at the kinds of mim substitution errors across type styles supports this hypothesis: In East Syriac, mims were mistaken as a qoph (twice in Script), a phé (once in Language), and an olaph (once in Language). In Serto, by contrast, mims were misidentified with six (different) letters. In Serto-Language, mims were recognized as a lomadh (1), a waw-béth (1), and a koph-lomadh (1); and in Serto-Script, mims were recognized as a koph-combination (3),59 a béth (2), a waw (1), a lomadh (1), a waw-lomadh (1), a ḥéth (1), and an ayn (1).60 A mim’s shape in Serto is similar to greater variety of letters than in the other type styles; particularly in the font W64, mims can look like a rounded letter followed by a tooth letter. Given that even human readers might mistake the mim as a different set of letters, it is perhaps unsurprising that Tesseract was occasionally confused.

Figure 11: W64 Serto mim

A second issue arises when considering the great disparity between Serto-Script and the five other Tesseract permutations. A nearly 87% accuracy rate is not unsatisfactory, but what makes this error rate so intriguing is the fact that it differs so markedly from the five other type style-mode combinations. What would make Serto mims particularly confusing to the Script mode but not to the Language mode? While the shape of Serto mims seems to be an issue for Tesseract, if shape were the only problematic variable one should expect to see it causing similar problems for Tesseract in both modes, not only one. Evidently, some aspect of the Script command-line argument (-l script/Syriac) contributes to this misreading.

Rarely were other letters read as mims. A shin became a mim once in East Syriac-Script. A mim was substituted for a koph once in Serto-Language. A waw was recognized as a mim once in both Serto-Language and Serto-Script. Estrangela did not substitute a mim for any letter in either mode.

Added yudhs have already been discussed in detail under Added Tooth Letters. However, it bears repeating that this particular error was unique to Serto. Tesseract added a high number of yudhs in Serto (41 and 84 in Language and Script modes, respectively), but the other two type styles had a miniscule number (three and eight total for Estrangela and East Syriac, respectively).

Attentive readers will have noticed that Serto tends to have distinct error trends compared with the Estrangela and East Syriac type styles. Tooth letters, particularly yudhs, were added erroneously in all type styles but at a far higher rate in Serto. Tesseract misread mims to a higher degree in Serto. Serto had the lowest diacritic accuracy of all three type styles. While stray marks in the Serto images may have contributed to some of these OCR errors, they cannot account for all of Tesseract’s mistakes. Since Serto images had essentially identical resolutions to those in Estrangela and East Syriac, pixel size cannot account for this discrepancy either.

The most likely explanation for Tesseract’s unusual error patterns in Serto lies at the level of font. The print types tested in this paper were chosen based on popularity—because they were frequently-used print types in each type style. As the analysts discovered partway through the testing project, Tesseract was most assuredly trained on Meltho fonts, and thus, print types closely related to Meltho fonts will perform better with Tesseract. Print types S14 and E22, those tested here for Estrangela and East Syriac, happened to be part of the set of types used as the basis for the Meltho computer fonts. Their closeness in shape to Tesseract’s training modules explains their OCR superiority in these areas. However, the Serto print type tested, W64, was not part of the set of print types upon which Meltho fonts were based, making its letter shapes further from Tesseract’s baseline. By correlation, Tesseract is less able to recognize text printed in W64. This explains much of the broadest level results between Estrangela, East Syriac, and Serto. If Tesseract were trained with a font based on W64, Tesseract’s OCR accuracy with Serto print type would likely improve significantly.

East Syriac still had the lowest average consonantal accuracy (88.87%, averaging the two modes), slightly below Serto (91.33%, averaged) and far behind Estrangela (98.89%, averaged). Yet E22 was part of the pool of print types underlying the Meltho fonts. How can its low performance be explained? Very simply, as it turns out. The shapes of letters in the East Syriac type style are more similar in shape to one another than the same letters are to each other in Serto and Estrangela type styles, which show a greater differentiation of letter design. Dolaths and rishes in particular, are uniquely shaped in the E22 print type and look very similar to kophs, béths, waws, hés, phés, and qophs. Errors with dolath and rish makeup 25.99% and 20.78% of consonantal errors in East Syriac-Language and East Syriac-Script, respectively.61 Because more letters look far more alike in East Syriac than in the other type styles, it is more difficult for Tesseract to distinguish between them.

The three pages tested from Damascus 12/21 contained a total of 1,924 consonants. There were 553 consonantal errors in Language mode and 630 consonantal errors in Script mode. With all these errors taken into account, the consonantal accuracy rate for the Language mode is 72.30% and the consonantal accuracy rate for Script is 67.26%. These results are significantly lower than the consonantal accuracy rate for the printed Estrangela texts that were tested, which were recognized with over 98% accuracy in both modes.

Tesseract generated significantly more errors of each type for Damascus 12/21 than for the printed texts, but the low accuracy was primarily the result of the high number of consonants deleted due to line truncation. As Table 26 shows, truncated characters were by far the most frequent error, accounting for over half of the errors in both modes. As this error is possibly caused by issues with the input image, an accuracy rate was also calculated which excluded truncated characters from both the total consonant and the total error counts. When truncated characters are removed from the calculation, the accuracy rate raises significantly to 83.34% in Language and 83.32% in Script.

Plates 29, 34, and 35 contained a total of 1,770 consonants. 240 errors were recorded in Language and 160 recorded in Script. With all these errors taken into account, Tesseract was able to correctly recognize 86.44% of consonants in Language mode and 90.96% of consonants in Script mode. These accuracy rates are still lower than those for the printed pages, but they are much higher than the accuracy ratings for Damascus 12/21. This increase in accuracy is attributable in part to the far-lower number of truncated characters, but the number of errors recorded for the other three categories decreased as well. When truncated characters are removed from the calculation, the consonantal accuracy rate raises slightly to 89.37% for Language and 91.74% for Script.