Image Preprocessing for Improving OCR Accuracy

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MEMSTECH’2007, May 23-26, 2007, Lviv-Polyana, UKRAINE 
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Image Preprocessing for Improving OCR Accuracy 
Wojciech Bieniecki, Szymon Grabowski and Wojciech Rozenberg 
 Abstract - Digital cameras are convenient image 
acquisition devices: they are fast, versatile, mobile, do not 
touch the object, and are relatively cheap. In OCR 
applications, however, digital cameras suffer from a 
number of limitations, like geometrical distortions. In this 
paper, we deal with the preprocessing step before text 
recognition, specifically with images from a digital 
camera. Experiments, performed with the FineReader 7.0 
software as the back-end recognition tool, confirm 
importance of image preprocessing in OCR applications. 
 Keywords – Image preprocessing, OCR, digital cameras. 
I. INTRODUCTION 
 Optical character recognition belongs to the most important 
image analysis tasks. Its main applications are in building 
digital libraries (containing text, math formulas, music scores 
etc.), recognizing objects on digitalized maps, localization of 
vehicle license plates [1], text readers for visually impaired 
people, understanding hand-written office forms, bank checks 
etc. A typical OCR system consists of the following steps: 
• image preprocessing, e.g. noise attenuation, correction of 
image orientation; 
• image binarization, usually performed adaptively [2]; 
• segmentation [3], usually hierarchical (recognizing page 
layout, detecting text areas (and tables, figures etc.), then text 
paragraphs, individual lines, then segmenting lines into 
words, and finally words into characters); 
• actual recognition (supervised or unsupervised) [4, 5]; 
• spellchecker-guided postprocessing; 
• saving output in some popular format (html, PDF, LaTeX). 
We noticed that mainstream commercial OCR tools were 
optimized for scanner-captured rather than camera-captured 
documents [6]. This goes against the trends of recent years, in 
which digital cameras dominated the market of acquisition 
devices. Digital cameras are fast, versatile, mobile, and 
relatively cheap. Moreover, they do not touch the 
photographed object, which is sometimes very convenient. 
However, cameras also have some drawbacks. They suffer 
from geometrical distortions (e.g., when photographing a 
thick volume), focus loss and uneven document lighting. 
In our work we assume typewritten documents, acquired from 
a digital camera. 
II. GOALS OF IMAGE PREPROCESSING BEFORE OCR 
In our previous work [6] we ran a simulation of OCR 
detection under controlled influence of noise, geometric 
distortions, varying image resolution and lighting conditions. 
The experiments, measuring OCR accuracy of the FineReader 
7.0 software, were carried out on the following images: 
- a formatted page of a MS Word document saved as a 600 
dpi bitmap (simulation of a “perfect” image, ideal conditions); 
- same image, image resolution decreased to 300 dpi and 150 
dpi, respectively; 
- same image (600 dpi and 300 dpi), affected with 10%, 20% 
and 30% Gaussian noise (simulating low-lighting conditions 
and resulting CCD matrix noise); 
- previous image filtered with the classic median noise 
removal filter; 
- original image (600 dpi) under typical geometric 
distortions: rotation, skew or “fish-eye” (simulating photos 
taken from a bad angle, wide zoom); 
- original image (600 dpi) affected with uneven lighting. 
A couple of conclusions were drawn from those experiments. 
The OCR process was not much hampered by uneven 
lighting. Noise was much more detrimental, but for 10% 
amount of noise and high resolution (600 dpi) the accuracy 
still remained viable. The median filter appeared totally 
inappropriate for our scenario, resulting in further accuracy 
loss. The FineReader accuracy dramatically drops for image 
resolution below 300 dpi. But, interestingly, upsampling a 
low-resolution image results in significant accuracy 
improvement. 
Nonetheless, OCR recognition was most sensitive to 
geometric deformations. These types of artifacts make it 
difficult to trace text lines, which may imply, that further 
OCR processing of an image rotated by e.g. 10 degree totally 
fails. Unlike noise and low resolution obstacles, geometric 
deformations may be (in theory) eliminated once we are 
aware they may appear. 
III. IMAGE AUTOROTATION 
 A common problem in an early stage of OCR preprocessing 
is to adjust the orientation of text areas. The text lines should 
be parallel to image boundaries. In this section we present an 
algorithm solving this problem. Our idea is to find a straight 
line supporting the left margin of the found text area (Fig. 1). 
The slope of this line defines the page rotation angle. 
In the beginning, the acquired image is converted to binary 
(using im2bw function from Matlab’s Image Processing 
Toolbox), where value 0 indicates a black pixel, to speed up 
further processing. Then, every k-th horizontal line is 
scanned, from left to right, and the first occurring black pixel 
on each scanned line is stored in the array Tab. 
 
 
W. Bieniecki, Sz. Grabowski, W. Rozenberg – Computer 
Engineering Dept. Technical University of Lodz, al. Politechniki 
11, Lodz, POLAND. E-mail: wbieniec@kis.p.lodz.pl 
 
MEMSTECH’2007, May 23-26, 2007, Lviv-Polyana, UKRAINE 
76
 
( ) ( ) ( )( )nn yxyxyxTab ,,,,,, 2211 K= (1) 
 
Fig. 1. Margin line detection 
 
Smaller values of k improve the accuracy but naturally slow 
down the processing. We experimentally set k to 5. 
To the next stage of the procedure we must select pixels 
belonging to the left boundary only. The image may be 
rotated clockwise or counterclockwise. In the first case we 
should remove points belonging to the bottom line of the text 
(Fig. 2). 
 
Fig. 2. Selection of the points belonging to the bottom line. 
The text is clockwise rotated. 
 
In a common case (assuming clockwise rotation of the text) 
we initially build two sets of points satisfying the condition: 
( ){ }iikk xxniyxTab >=∀= −11 ,1:, K 
12 \ TabTabTab = (2) 
Then we choose the set which represents the line that is “more 
vertical”, i.e. the larger of those two sets, assuming the text 
orientation is “portrait” rather than “landscape” one. 
⎩
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TabTabifTab
TabN
2
211 . (3) 
 
In the text step, we eliminate the points indicated red in Fig. 
3. 
 
Fig. 3. Black points define the left text boundary. 
Red points are misleading and should be eliminated. 
The criterion for these points is the average displacement of x 
coordination for the successive points from TabN. We denote 
the remaining set as TabS. 
 
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Then we select two points from TabS, pmin and pmax, having 
respectively the minimum and the maximum x coordinate in 
TabS. Having found them, we evaluate the line slope and the 
angle for the rotation. 
 
 
Fig. 4. Knowledge of the extreme points pmin and pmax 
makes the rotation trivial 
IV. PERSPECTIVE CORRECTION 
Sometimes the image acquisition device (esp. a digital 
camera) is positioned not orthogonally to the paper sheet. As 
a result, the acquired area is not a rectangle, but rather a 
trapezoid or a parallelogram. Experiments show that 
 
MEMSTECH’2007, May 23-26, 2007, Lviv-Polyana, UKRAINE 
77
 
especially the latter sort of distortion is harmful for the OCR 
process. 
 
Fig. 5. Image distortion due to improper camera positioning 
 
The first step toward image perspective correction is pointing 
out the quadrilateral containing the text scope. This operation 
can be carried out manually or with aid of the technique from 
the previous section. 
 
Fig. 6. Finding the bounding rectangle 
 
Let I be the image containing the whole page, and define the 
following points of the quadrilateral: 
( ) ( )
( ) ( ) bottomrightyxpbottomleftyxp
toprightyxptopleftyxp
pppp
pppp
_,_,
_,_,
444333
222111
======== (6) 
Initially, the new dimensions of the rectangle bounding the 
text area are evaluated: 
2
,
2
41233412 ppNppN YX
Δ+Δ
=
Δ+Δ
= . (7) 
where ∆pij is the length of the line segment pipj. 
For all of the points of the new image containing the text area 
( ){ }YXN NjNijiI ...1,...1:, === the color of the pixel is 
interpolated. 
( )
X
pXp
j N
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p 12
1
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= 
( )
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xiNxi
p 41
4
⋅−+⋅
= ,
( )
Y
pYp
i N
yiNyi
p 41
4
⋅−+⋅
= 
(8) 
Formula (9) evaluates the coordinates (x,y) of the point from 
the original image which will be copied onto the (i,j) pixel in 
the new image. 
( ) ( ) ( ) (
( ) ( ) ( ) ( )
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−⋅−−−⋅−
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−⋅−⋅−−⋅−⋅=
 
(9) 
 
V. NON-LINEAR IMAGE TRANSFORMATION 
The need for a non-linear image transformation is easily 
understandable if we look at a photograph of an open book 
(Fig. 7). 
 
 
Fig. 7. The image of scanned book 
 
The OCR system fails, because the text lines are curved and 
near the centre the letters are narrower. 
The first step toward the correction is to trace the curves 
bounding the upper and lower borders of the text area. We 
assume that the left and right borders are straight lines. We 
interpolate the curves using splines with four knot points. In 
the currect implementation the lines are adjusted manually. 
 
 
Fig. 8. The process of adjustment of the bounding curves 
 
 
MEMSTECH’2007, May 23-26, 2007, Lviv-Polyana, UKRAINE 
78
 
Having pointed out both upper and lower curve, the actual 
correction procedure is run. Each point of the spline curve is 
described by parameter [ ]1;0∈t . For a specific value t, the 
straight line between upper and lower curve is evaluated. The 
length of the line between starting points ( ) ( )( )0,0 uu yx and 
( ) ( )( )0,0 ll yx is the pattern height of the new image. Each line 
( ) ( )( )tytx uu , is then transformed to the perpendicular line 
using the interpolation equations (see previous section). As a 
result, a rectangle image is created. 
 
 
Fig. 9. Finding the bounding curves of the region to process 
 
 
Fig. 10. The result of image restoration 
 
VI. EXPERIMENTAL EVALUATION 
In all the tests, FineReader 7.0 was used as the OCR software. 
The first experiment was conveyed on a scanned A4 page, 
containing text from a Polish book. There were 2698 
characters (excluding whitespaces) on the page. In these tests, 
defects were added artificially, with aid of Paint Shop Pro 9 
image processing software, and Matlab scripts. 
Table 1 shows how the image resolution affects the OCR 
accuracy. Note that, suprisingly, very high resolution (600 
dpi) leads to accuracy deterioration, which is probably some 
peculiarity of the used software (we suspect that the FR 7.0 
recognition algorithm is tuned for 300 dpi images). 
In the next experiment noise of varying intensity was added 
(imnoise Matlab’s IPT function) to the test image of 300 dpi. 
We tried: speckle, salt & pepper (s&p) and white Gaussian 
noise. The deteriorated images were then processed with the 
filters. Detailed results are presented in Table 2. The median 
filter for samples affected with speckled noise as well as 
Gaussian gives unstable results, sometimes hampering further 
OCR process significantly. Only s&p noise may be 
successfully attenuated. 
TABLE 1 
OCR ERRORS FOR DIFFERENT IMAGE TYPES 
AND RESOLUTIONS 
image resolution (dpi) error [%] 
scanned 75 0.96 
scanned 100 0.14 
75dpi, interp. 150 0.59 
scanned 150 0.03 
100dpi, interp. 200 0.07 
scanned 200 0.22 
150dpi, interp. 300 0.11 
scanned 300 0.07 
600dpi, interp. 300 0.11 
200dpi, interp. 400 0.07 
scanned 600 0.40 
 
TABLE 2 
OCR ERRORS FOR NOISY IMAGES, FILTERED OR UNFILTERED 
noise filter error 
[%] notes 
s&p, 10% – – text area error 
s&p, 10% median, 3x3 0.03 – 
s&p, 10% Wiener, 5x5 0.07 – 
s&p, 20% – – 
text area err., 
language 
misrecogn. 
s&p, 20% median, 5x5 0.14 – 
s&p, 30% – – unrecogn. text 
s&p, 30% median, 5x5 0.44 – 
s&p, 30% median, 7x7 1.63 – 
speckle, 10% – 0.81 – 
speckle, 10% median, 3x3 0.07 – 
speckle, 10% median, 5x5 0.11 – 
speckle, 10% Wiener, 5x5 0.18 – 
speckle, 10% min, 3x3 1.03 – 
speckle, 10% thresh. 140 + 
max, 3x3 0.14 – 
speckle, 20% – – unrecogn. text 
speckle, 20% median, 5x5 0.40 
speckle, 20% median, 7x7 1.18 im. too dark 
speckle, 20% Wiener, 5x5 0.37 – 
speckle, 20% Wiener, 7x7 1.14 im. too dark 
Gauss., 30% – 0.11 – 
Gauss., 40% – – text area error 
Gauss., 40% median, 3x3 – text area error 
Gauss., 40% min, 3x3 0.14 – 
Gauss., 40% 
median, 3x3 + 
intensity 
adjustment 
0.11 imadjust([0.8 1], 
[0 1]) 
Gauss., 40% 
median, 3x3 + 
intensity 
adjustment 
0.00 imadjust([0.9 1], 
[0 1]) 
 
In the next experiment the input image was rotated by a 
known angle before scanning. Three correction types have 
 
MEMSTECH’2007, May 23-26, 2007, Lviv-Polyana, UKRAINE 
79
 
been considered: FineReader’s automatic one, “manual” 
correction (i.e. we explicitly set the rotation angle) and 
automatic in our application. The results are shown in Table 
3. 
The FR 7.0 can easy cope with slightly rotated images, but if 
the rotation angle exceeds about 10º, then the text area is 
completely misdetected. 
 
TABLE 3 
OCR ERRORS FOR A ROTATED AND THEN CORRECTED IMAGE 
image correction 
method 
error 
[%] notes 
right rot., 3º FR, auto 0.07 some chars 
in italic 
right rot., 3º auto 0.03 – 
right rot., 3º manual 0.03 – 
right rot., 5º FR, auto 0.59 – 
right rot., 5º auto 0.11 – 
right rot., 5º manual 0.03 – 
right rot., 15º – – recogn. area 
misdetected 
right rot., 15º auto 0.07 – 
right rot., 15º manual 0.07 – 
left rot., 15º – – recogn. area 
misdetected 
left rot., 15º auto 0.11 – 
left rot., 15º manual 0.11 – 
left rot., 90º – – unrecogn. text 
left rot., 90º manual 0.03 – 
 
Perspective correction was the goal of the next test. We used 
Paint Shop Pro 9 to transform a text page into a trapezoid or a 
rhomboidal shape. Fig. 11 illustrates the three considered 
perspective destortions. OCR errors on (corrected with the 
algorithm from Section IV) image affected with these types of 
distortions are given in Table 4. 
 
Fig. 11. Perspective distrortions considered in the tests: 
a) trapezoid, b) reverse trapezoid, c) rhomboid. 
 
TABLE 4 
OCR ERRORS AFTER PERSPECTIVE DISTORTIONS 
distortion correction 
method 
error 
[%] notes 
trapezoid – 0.70 – 
trapezoid our alg. 0.07 – 
rev. trapezoid – – recogn. area 
misdetected 
rev. trapezoid our alg. 0.11 – 
rhomboid FR’s auto – recogn. area 
misdetected 
rhomboid our alg. 0.03 – 
 
Non-linear distortions were also added programmatically. 
Table 5 refers to the three cases showed in Fig. 12 (a, b, c). 
 
Fig. 12. Non-linear distrortions 
 
TABLE 5 
OCR ERRORS AFTER NON-LINEAR DISTORTIONS 
distortion correction 
method 
error 
[%] notes 
Fig. 12a model – – recogn. area 
misdetected 
Fig. 12a model our alg. 0.11 – 
Fig. 12b model – – recogn. area 
misdetected 
Fig. 12b model our alg. 0.14 – 
Fig. 12c model – – recogn. area 
misdetected 
Fig. 12c model our alg. 0.14 – 
It is clear from Tables 4 and 5 that FR 7.0 cannot operate on 
images affected with these types of distortion. On the 
contrary, our algorithm removes those distortions almost 
perfectly (practically no accuracy loss in the further OCR 
stage). 
The final experiment was probably the most valuable one. We 
preprocessed a real photograph taken from a digital camera 
(Fig. 13), presenting an open book. The image resolutionwas 
2288 x 1712 px. The text to recognize was in Polish language, 
it contained 1651 characters (excluding whitespaces). The 
image contained several defects: wrong perspective, 90º right 
rotation, non-linear geometrical distortions, bad contrast, 
smudges in the background (paper). 
 
MEMSTECH’2007, May 23-26, 2007, Lviv-Polyana, UKRAINE 
80
 
 
Fig. 13. Photograph of an open book. Many defects visible 
 
The correction procedure contained the following steps (in 
this order): 
– 90º left rotation, 
– perspective correction, 
– non-linear distortion correction, 
– image intensity adjustment. 
The output result is presented in Fig. 14. 
 
 
Fig. 14. Corrected image 
 
TABLE 6 
OCR ERRORS ON THE REAL DIGITAL CAMERA IMAGE 
input image correction 
method 
error 
[%] notes 
original – – text area not 
found 
original 90° left rotat. – recogn. area 
misdetected 
rotated pers. corr. 2.72 – 
rotated + pers. corr. non-linear 0.96 – 
defect corr. 
rotated + pers. corr. + 
non-linear defect corr. 
intensity 
adjustment 0.88 imadjust 
([0 0.8] , [0 1]) 
 
FineReader 7.0 was unable to process the original input 
image, most importantly due to perspective distortions. As 
Table 6 shows, with each successive algorithm layer, the 
accuracy improves, to reach less than 1% error. 
VII. CONCLUSION 
 The optical character recognition is an active research area, 
but most efforts concern the actual recognition, not the 
preprocessing phase. In this work, we showed that appropriate 
image preprocessing for OCR is extremely important 
especially for digital camera acquired images. Non-
professional digital cameras are rapidly becoming the main 
source of image information, also in case of text scanning. We 
predict that the sort of algorithms proposed in this paper will 
be embedded in the future versions of OCR software. 
REFERENCES 
[1] Bubliński Zb., Mikrut Zb.: Lokalizacja tablic rejestra-
cyjnych pojazdów. Automatyka, t. 7, zeszyt 3, Uczelniane 
Wydawnictwa Naukowo-Dydaktyczne, Kraków 2003, pp. 
303–312 (in Polish). 
[2] Thillou C., Gosselin B.: Robust thresholding based on 
wavelets and thinning algorithms for degraded camera 
images. Proceedings of ACIVS 2004, (2004). 
[3] Mao S., Rosenfeld A., Kanungo T.: Document structure 
analysis algorithms: a literature survey. Proc. SPIE 
Electronic Imaging. Jan. 2003; 5010: 197–207. 
[4] Tanghva K., Borsack J., Condit A.: Evaluation of model-
based retrieval effectiveness with OCR text. ACM Tran-
sactions on Information Systems, 14(1): 64–93, Jan. 1996. 
[5] Kauniskangas H.: Document image retrieval with impro-
vements in database quality. Oulu University Library, 
ISBN: 951-42-5313-2. ISBN: 951-42-5313-2, 1999. 
[6] Bieniecki W.: Analiza wymagań dla metod przetwarzania 
wstępnego obrazów w automatycznym rozpoznawaniu 
tekstu. Automatyka, t. 9, zeszyt 3, Uczelniane Wydawnic-
twa Naukowo-Dydaktyczne, Kraków 2005, pp. 525–532 
(in Polish). 
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 /ITA (Utilizzare queste impostazioni per creare documenti Adobe PDF adatti per visualizzare e stampare documenti aziendali in modo affidabile. I documenti PDF creati possono essere aperti con Acrobat e Adobe Reader 5.0 e versioni successive.)
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 /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken waarmee zakelijke documenten betrouwbaar kunnen worden weergegeven en afgedrukt. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
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 /ENU (Use these settings to create PDFs that match the "Required" settings for PDF Specification 4.0)
 >>
>> setdistillerparams
<<
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>> setpagedevice