Department Of ECE, CBIT Hyderabad- 500 075. INDIA

Head, Engineering Research, Electrical Sciences, Amrita University,

Coimbatore, INDIA .

—

Keywords- Palm Leaf Character Recognition (PLCR), Radon Transform, 3D Features, Telugu Characters, Nearest Neighborhood Classifier (NNC).

I. INTRODUCTION

Character recognition (CR) is one of the oldest applications of automatic pattern recognition. The various applications of character recognition are in library automation, banks, defense organizations, reading aid for the blind, post offices, language processing and multi-media design. Hence the research in character recognition is very popular. To recognize Hand-Written Characters (HWC) is an effortless task for humans, but for a computer it is an extremely tricky job. This is mainly due to the vast differences or the impreciseness associated with handwritten patterns written by different individuals [1]. Machine recognition involves the ability of a computer to receive input from sources such as paper and other documents, photographs, touch screens and other devices, which is an ongoing research area. Handwritten character recognition (HWCR) can be divided into two categories, namely, Offline Handwritten Character recognition where the image is sensed “off-line” from a part of a document and “Online” Handwritten Character recognition where the movements of

the pen/tip can be recorded “on-line” as used in the pen-based computer screen systems [2]. On-line recognition provides the dynamic characteristics of the writing and conveys temporal or dynamic information such as the number and order of pen-on and pen-off movements, the direction and speed of writing and in some cases, the pressure applied while writing a character etc. Off-line recognition usually requires imperfect pre-processing techniques prior to feature extraction and recognition stages. Automated recognition of offline handwritten script has numerous [1] applications like, writer identification, handwritten text digitization, form reading, copies of engraving, reading old manuscripts .

Palm leaf manuscripts contain religious texts and treaties

on a host of subjects [3] such as art, medicine, astronomy, astrology, mathematics, law and music. It is extremely difficult to store these manuscripts for the years to come. The main causes of deterioration are climatic factors, (relative humidity, temperature) light and insects [3].

The non existence of standard / benchmark databases is

the biggest difficulty to do research on handwritten character recognition of Indian scripts [4]. Small databases collected in laboratory environments have been reported in previous studies [4].

The handwritten character recognition accuracy for

Indian scripts [5] is reported to be quiet low (approximately 60%) in the literature even on a medium like paper (in comparison to other media like cloth, stone and palm leaves etc.).

A. Difficulties encountered in handwriting recognition

The huge number of influencing factors in HWCR relate directly to the differences in conditions for experimentation. One of the main differences is the type of handwriting database used for experimentation. In some cases, researchers have constrained their experiments heavily, only using one person’s handwriting, while other researchers’ experiments were not performed on benchmark databases.

The greatest difficulty encountered in handwriting recognition lies, in the freedom the user takes when he writes. Irregular handwriting aggravates ambiguities described as above and makes it harder to group symbols and

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to distinguish relations among them. It results in “Layout” problems affecting the recognition of the whole expression. A reason for this is due to inexperienced users, because they normally take excessive freedom with the location and alignment of handwritten symbols. Other kinds of irregular writings arise during the correction, deletion, and insertion of symbols [6]. A situation like this could generate a very complex expression, which cannot be recognized even by humans [6] as shown in the figure 1.

Figure 1 Example of freely written handwritten document.

Challenges faced for preprocessing, deal with the choice of whether to convert raw handwriting into a more efficient form i.e. whether to binarize the handwriting or keep it in grey-scale form. Another issue is whether the handwriting should be thinned or should remain the way it is to preserve the features. Feature extraction further poses the problem of choosing the right features to extract and the right technique to perform the task. For example researchers may choose between extracting features such as the entire contours of characters or by extracting many features such as end-points, loops, holes and so on.

Finally, the task of finding a suitable classification

technique (for individual characters and whole words) has been exhaustively pursued. However, again the variability of handwriting and the lack of reliable feature extraction and preprocessing techniques have impaired many unconstrained approaches. For most of the aforementioned problems, including feature extraction and classification there are additional complexities associated with pre-processing steps like removing noise, skew correction and segmentation.

II. CHALLENGES WITH INDIAN LANGUAGES (LIKE TELUGU)

The recognition of off-line handwritten characters of different scripts was carried out extensively during last few decades. Some of these available works include for English [7], Chinese [8], Arabic [9]and Kanji scripts[10]. The progress of character recognition in Asian and particularly Indian scripts is in a relatively nascent stage [11, 12, 13] as

compared to English, which is in a mature stage of development due to the following reasons:

Compared to English, Indian languages have composite characters. Because of the sheer, number of English speakers, OCR in English is highly developed. With smaller number of speakers, languages like Telugu have not attracted equivalent efforts. In Telugu, consonants take modified shapes when attached with the vowels. Additionally, vertical extent of the character varies depending on the modifying vowel or consonant. Such characters are even more difficult for a machine to recognize. Non-uniformity in the spacing of the characters within a word due to the presence of consonant conjuncts (vowel + consonant) makes HWCR more difficult. Also, the presence of consonant conjuncts results in improper line segmentation. Recognition programs need to perform further processing to segment the lines. In scripts like Devnagari, all the characters in a word are connected by a unique line called shirorekha (also called head line). Word separation and line separation is easy in these cases.

Telugu is one of the prominent scripts in India with

more than 65 million worldwide speakers [11]. There are 18 vowels, 36 Consonants, and three dual symbols in this language. Some of the Telugu characters (vowels) are shown below in table 1 along with their pronunciation in English.

TABLE 1 Pronunciation of Telugu vowels

Many standard databases such as NIST, MNIST,

CEDAR, and CENPARMI are available for Latin numerals [4] On the other hand, to the best of our knowledge there are no standard databases for palm leaf character recognition. The non existence of standard / benchmark databases is the biggest difficulty to do research on handwritten character recognition of Indian scripts [4]. Small databases collected in laboratory environments have been reported in previous studies [4]. Therefore a small database generated in our lab has been used in the present work by measuring the pixel co-ordinates (X, Y, Z) of palm leaf characters.

Aa Aaa E Ee U Uu Ru Ruu

Ae Aae Ei O Oe ow Am Aha

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III. RADON TRANSFORM

Let ( ) = ( , ) be a continuous function vanishing outside some large disc in the Euclidean plane R2. The Radon transform [14], , is a function defined on the space of straight lines in R2 by the line integral along each such line:

( ) ( ) (1)

Concretely, any straight line can be parameterized by

( ( ), ( ) (( sin cos ), ( cos sin )) (2) Where is the distance of from the origin and is the angle the normal vector to makes with the axis. It follows that the quantities ( , ) can be considered as coordinates on the space of all lines in R2, and the Radon transform can be expressed in these coordinates by

( , ) ( ( ), ( ))

= (( sin cos ), ( cos sin ))

(3)

More generally, in the -dimensional Euclidean space “R , the Radon transform of a compactly supported continuous function is a function on the space of all hyper planes in R . It is defined by

( ) ( ) ( ) (4)

for , where the integral is taken with respect to the natural hyper surface measure, (generalizing the | | term from the 2-dimensional case). If any element of is characterized as the solution locus of an equation . (5)

Where 1 is a unit vector and . Thus the -

dimensional Radon transform may be rewritten as a function on 1×R via

.

( , ) ( ) ( ) (6)

It is also possible to generalize the Radon transform still further by integrating instead over -dimensional affine subspaces of R . The X-ray transform is the most widely used special case of this construction, and is obtained by integrating over straight lines.

IV. DATA ACQUISITION AND DIGITIZATION Character recognition in general involves scanning /

capturing the image of a document and storing it in the computer system which is used as an input image to the

character recognition problem. However the input method is . After selecting any

basic Telugu character from the given palm leaf, some of its pixel points along the contour of the character is identified. The (x, y and z) co-ordinates for each of these pixel points are measured. Using the these co-ordinate values, the image of the character is obtained on tothe computer to store and process it further. These images are binarized and are further normalized to a size of 50x50 pixels using minimum boundary rectangle method. Euclidean distance concept is used on these patterns to compare with the available patterns in the database of the computer. Palm leaves were provided by Oriental Research Institute (ORI), S.V. University Campus, Tirupati, Andhra Pradesh. For the present research, we have chosen palm leaves of two different scribers. Also two different types of palm leaves (one soft and one hard) were selected for the investigation. The photographs of the palm leaves are shown in figure 2 wherein the red arrow depicts the holes of the Folio which helps to store the leaves between the wooden boards.

Figure 2 Palm leaves chosen for the study . The flow chart describing the procedure for data

acquisition of the proposed method is shown in figure 3, and can be described as follows:

Step 1: Identifying the pixel positions for the character selected ranging from 13 to 30 pixels based on the character. The left most pixel is chosen as the origin O (0,0) coordinate and with reference to this point all the other pixel coordinates are measured using Measuroscope. Step 2: The x and y coordinates are measured using NIKON MEASURESCOPE Model No. 20 with NIKON SC-102 as the display. Step 3: Z coordinate is measured using a plunger type dial gauge indicator and SYLVAC 50 digital reader.

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Figure 3 Procedures for Image Acquisition The Z-dimension is the depth of indentation proportional

to the pressure applied by the scriber using stylus at each pixel point of the Telugu character [11,12, 13]. This Z- dimension is an added 3D feature used to obtain better recognition accuracy for the characters in a medium like palm leaf only. The set up used for the measurement of Z-Dimension is shown in the figure 4. A special needle is fabricated made up of Teflon which is attached to the dial indicator plunger. This needle is so designed that it does not damage the palm leaves during the measurements at various pixel points. The measurements were also taken with utmost care and precision. The needle attached to the dial indicator plunger is first positioned at the pixel point where the Z- Dimension is to be measured for any character.

Figure 4 Set Up Showing the Measurement of Z- Dimension

In the first step the distance of the bottom of the pixel

point is measured and recorded. This distance is termed as D1. Then this needle is placed on the surface of the palm leaf nearer to the pixel point and again the distance is measured called D2. The depth of indentation D for the selected pixel point is found by subtracting D2 from D1. Hence the depth of indentation at any pixel co-ordinate is D = D1 - D2. This procedure is repeated at all the pixel points of the Palm Leaf Character where X and Y measurement were taken. Hence for every Palm leaf character at any pixel point there are 3 dimensions X, Y and Z. Table 2 and 3 shows a sample set of readings of Aa and Tha respectively.

TABLE. 2: XYZ coordinates of Aa

TABLE 3: XYZ coordinates of Tha

The various steps in the preprocessing stage of the image obtained from chart wizard are as follows:

1. The image is first copied into a file of and an image name is assigned.

2. Using the concept of minimum boundary rectangle and the rectangular cropping tool of the

the image is cropped. This image is first changed to gray scale mode and then brightness and contrast is adjusted to maximum value. The mode of this image is then changed to bitmap with a final resolution of 72 pixels / inch obtained by diffusion dither method.

3. In the next step the size of the image is converted to a 50 X 50 pixel size using Bicubic method, each image contained in a height and width of 50 pixels each. This image is further stored at the appropriate folder in the system.

4. The process described in the above steps is repeated for all the images of all the characters using all the three types of images (XY, YZ and ZX).

IDENTIFYING THE PIXEL POSITIONS FOR THE CHARACTER SELECTED

MEASURING Z-COORDINATE / DEPTH OF THE INSCRIBED LETTER ON THE PALM LEAF AT

VARIOUS PIXELS USING A DIAL GAUGE INDICATOR

SELECTING A BASIC CHARACTER

MEASURING X, Y COORDINATES USING MEASUROSCOPE

PALM LEAF

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In the first step, the preprocessed images of size 50x50 are loaded as “database images” and “test images” which are independent to each other. They are binarized with a threshold value of 0.7. The Radon Transform of each image is found out and the absolute value is stored as a feature vector for all the “database images”. The resulting matrix obtained is reshaped into a column matrix for each image. This process is repeated for “test images” also. The test images and database images of each palm leaf character are independent to each other. The Euclidean distance between the feature vector of the test image and each of the database images is computed. Next we find out the respective image of the database which has the shortest distance to the feature vector of the test image. The output display contains the test image on the left hand side and the image of the database which has the minimum distance on the right hand side. The test images are given as input serially one after the other and corresponding output images are obtained. We count the number of matched and mismatched characters to find the recognition accuracy.

A. Building up of Data base Present day Telugu language contains totally 54

isolated characters of which there are 18 vowels and 36 consonants. These palm leaves are approximately 700 years old and hence some of the present Telugu characters were not in use in those days. 29 different present day Telugu characters could be successfully retrieved from the palm leaves for the present work. Few of the characters like Am , Aha, Anya etc were excluded since they were rarely used in the set of palm leaves.

All the 29 characters were sampled at 5 different

positions as discussed in the earlier section. A total of 29 X 5 = 145 characters of each scriber totaling to 290 characters (for 2 scribers) were generated for hard type of palm leaves. Similarly for soft type of palm leaves 29 X 5 = 145 number of characters was generated. So totally 435 characters were generated in the proposed method.

The pixel co- ordinates i.e. X, Y, Z for all these

characters (435 characters) were measured. Images of two dimensional patterns in XY, YZ and ZX planes were generated as described in earlier section. Hence 435 X 3 planes = 1305 images are used in our investigations.

A group of characters of Telugu have a high level of

correlation coefficient of 0.75 and above creating lot of confusion in the normal XY plane, where as in the YZ plane, the patterns are completely different from each other as shown in the figure 5 and figure 6 respectively. These images are examples of the characters pertaining to a single subgroup. This means that the 3D feature helps the recognition of a Palm leaf character.

Ae in XY plane Na in XY plane Va in XY plane

Figurer 5 Characters in the XY plane

Ae in YZ plane Na in YZ plane Va in YZ plane

Figure 6 Characters in the YZ plane

.

V. IMPLEMENTATION AND EXPERIMENTAL RESULTS OF RADON TRANSFORM

All the experiments were carried out on a PC machine

with P4 3GHz CPU and 512MB RAM memory under Matlab 7.0 platform. The images of various Telugu palm leaf characters are obtained by the data acquisition and digitization methods as discussed earlier. The performance of the proposed system of the recognition process was evaluated on the images obtained from the real data extracted from the palm leaves. The pixel co-ordinates of the palm leaf Telugu characters were systematically collected using the measureoscope and the apparatus described earlier. Using the pixel coordinate values (X, Y, Z) various patterns of the palm leaf Telugu characters are generated in the XY, YZ and XZ planes. Four samples of each class of Telugu characters with twenty nine different classes is used to train the database in the proposed system. A separate sample of each class is used to test the recognition accuracy of the proposed system. Hence the training sample set contained 116 samples (29X4=116) where as the test sample set contained 29 samples (29X1=29) of each class. The results obtained are discussed in the next section. All the images of the characters which are tested with the proposed method are normalized to a size of 50X50. The step by step algorithm is shown in the figure 7 below:

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Figure 7 Step by step algorithm for implementing Radon Transform

A. Experimental Results

The results obtained with the proposed system in XY plane are shown in figure 10. The first row contains 3 sets of images. The left hand side image of each set is the input

image whereas the image on the right hand side is the image in the database. All these images indicate that the test image has been correctly recognized after training the computer with only 4 samples of each class of Telugu palm leaf characters. The proposed method is invariant with respect to scale, rotation and also shift which is noticed in the figure 10. The second row of images consists of 2 sets of images. The left hand side image of each set is the input image whereas the image on the right hand side is the image in the database. These test characters are not identified correctly. The percentage accuracy of recognition obtained with the proposed method for XY, YZ and ZX planes are tabulated in table 4.

TABLE 4 Percentage accuracy of recognition for two sets of palm leaves

In the proposed method Radon Transform is used but

the third dimension i.e. Z (the depth in microns which is proportional to the pressure applied by the scriber at various pixel points) is considered. The images in the YZ and ZX planes contain this third dimension feature. In the results shown in the Table 4, it is very clear that the percentage of accuracy increases drastically due to the third dimension feature Z. The percentage of recognition accuracy in the YZ plane varies from 89 % to 93 %. In the ZX plane the percentage of recognition accuracy varies from 80% to 83 %. The comparison of the published [11, 12, 13] and the proposed system is shown in the table 5

The figure 8 describes the percentage of accuracy of palm leaf characters in XY, YZ and ZX planes of projection using radon transform. It is very clear that YZ plane of projection has the highest percentage of recognition. This is ue to the inherent property of the Telugu characters, since they have maximum variation in the Y direction. Also due to the Z direction feature as described earlier in this paper, the YZ plane of projection has the highest percentage of accuracy.

TABLE 5 Comparison between published and proposed methods for Palm Leaf characters.

(% Accuracy of recognition)

(% Accuracy of recognition)

76 71 89 93 80 83

40 43 54 79 76

40 96 90 90 89

37 96 70 69 80

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Figure 8 Percentage of recognition accuracy in the different planes

of projection for Radon Transform

Figure 9 Percentage accuracy in XY, YZ, ZX planes of different

transforms

Radon Transform is widely used for handwritten character recognition and hence this was incorporated in the proposed work. In the proposed method the co-ordinates of the pixels are measured and the depth of indentation (which is proportional to the pressure applied at that pixel point) in microns. The palm leaf characters are generated using the Microsoft excel and Adobe Photoshop from these pixels coordinates. The proposed method thus does not have the noise and skew related problems as scanning of the characters is avoided. The nearest neighborhood classifier is used as a classifier by finding the minimum Euclidian

distance between the given test image and the images in the data base. The figure 9 shows the percentage accuracy of recognition obtained using different transforms in different planes of projection. The figure 10 shows sets of different Palm leaf characters correctly identified. It contains 3 sets. The first image in each of the set is the input image where as the second one is the image matched in the data base. Some of the image sets which were not identified correctly are shown in figure 11.

Figure 10 The input and output images of various test samples in XY Plane correctly identified

Figure 11 The input and output images of various test samples in XY

Plane not correctly identified

VI. CONCLUSIONS AND FUTURE SCOPE

1. Palm Leaf Character Recognition (PLCR) using Radon Transform is explored in this work.

2. Using only the (X-Y) co-ordinates the recognition accuracy for even the Hand written paper documents is less than 60% as reported in literature . The Palm leaf characters have additional information in the form of depth measured in microns, which is proportional to the stylus pressure applied by the scriber. Hence (X, Y, Z) information was used for the PLCR.

3. YZ plane images for the palm leaf characters gives good results (above 90% recognition accuracy), justifying the use of Z-dimension.

4. Although some of the characters like Va, Ma, Ya, Pa or Na are very similar to each other, the proposed is able to identify them distinctly in YZ- plane of projection. This shows the importance of Z-dimension, a special feature of palm leaf characters.

5. The Palm Leaf Character Recognition (PLCR) is performed only on basic Telugu characters and hence can be extended to Samyukt aksharas ( combination of 2 or more basic characters ).

6. The method of data collection can be improved in future, by automated process of measurements like a 3D laser scanner instead of manual data collection mainly for the Z- dimension.

ACKNOWLEDGMENT The author whole heartedly acknowledges the co-

operation extended by Sri S. Anand, Finance Officer, RSVP (Rashtriya Sanskrit Vidyapeeth), Tirupati in procuring the

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palm leaves from Oriental Research Institute, Tirupati, A.P, India. Further, the author expresses sincere gratitude to Dr. Vally Maya who has actively participated in the technical discussions and rendered appropriate suggestions at every stage in the work.

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[6] Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].

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[11] Panyam Narahari Sastry, Ramakrishnan Krishnan, Bhagavatula Venkata Sanker Ram, Telugu Character Recognition on Palm Leaves-A three dimensional Approach Technology Spectrum (JNTU Hyderabad), Vol. 2, No. 3, pp.19-26, November 2008.

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