Handwritten Script Recognition at Block Level

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1 Chapter 4 Handwritten Script Recognition at Block Level Optical character recognition (OCR) is broadly defined as the process of recognizing either printed or handwritten text from document images. Many techniques have been presented in the literature to perform this task for a specific language; however, such OCR will not work for multilingual documents. Script identification makes the task of analysis and recognition of the text easier by suitably selecting the modalities of OCR. Most of the published work on automatic script identification of Indian scripts, deals with printed documents compared to handwritten script identification. In this chapter, a novel method for script identification from handwritten documents at block level is presented. Feature selection is done using Discrete Cosine Transform (DCT) and Wavelets of Daubechies family. The k-nn classifier is used for script recognition Introduction In most of the multi-script documents (printed or handwritten), an arbitrary block of a document consists of mono script (Fig. 2.2). In such cases, script identification at block level reduces the script identification task compared to script identification at line/word level which requires pre processing such as line/word extraction form the document before recognizing the script type. Although there are twelve major scripts in India and the multi-script/multilingual documents are quite common in Indian environment, there are no significant methods available in the literature for Indic script identification from handwritten documents at block level [1, 2, 3, 4, 5 30]. In this chapter, a novel method for script identification

2 52 from handwritten document images at block level is presented. The recognition of script is based upon features extracted from the text blocks using Discrete Cosine Transform (DCT) and Wavelets of Daubechies family. Supervised classifier, namely, k-nn, is adopted for script identification. 4.2 Feature Extraction Features are measurable quantities that maximize the distinction between different scripts. Features are extracted by transforming the text block into frequency domain. The term frequency refers to variation in brightness or color across the image, i.e. it is a function of spatial coordinates, rather than time. The frequency information of image is needed to see information that is not obvious in time-domain. The feature extraction procedure is described below. The text block of size 512 x 512 pixels (in gray scale) is binarized using Ostu s method so that text represents value 1 and background represents value 0. The salt and pepper noise around the boundary of the block is removed using morphological opening. This operation also removes discontinuity at pixel level. The text is then skeletonised by applying thinning operation. DCT is applied to the preprocessed block and image is divided into four non-overlapping regions. Standard deviation is computed for the first and second regions of the block. Next, Wavelet (Daubechies 9) decomposition is applied to DCT image and standard deviation for three sub bands is computed. For 2-D images, applying DWT corresponds to processing the image by 2-D filters in each dimension. The filters divide the input image into four non-overlapping multi-resolution sub-bands LL, LH, HL and HH. The sub-band LL represents the coarse-scale DWT coefficients while the sub-bands LH, HL and HH represent the fine-scale of DWT coefficients. Since the image is a binary image, the decomposition is carried out at only one level. The feature extraction algorithm is presented below.

3 53 Algorithm Input: Block of size 512 x 512 pixels in grey scale. Output: Feature vector representing the text block. Method: 1. Binarize the input image using Otsu method to obtain text representing binary 1 and background binary 0 (Fig. 4.1 (b)). 2. Remove small objects around the boundary using morphological opening. 3. Apply thinning operation (Fig. 4.1 (c)). 4. Apply DCT and divide the magnitude (image) of DCT into 4 equal nonoverlapping block and compute the Standard Deviation for the first and second block. This forms 2 features. 5. Perform Wavelet (Daubechies 9) decomposition for the magnitude (image) of DCT to obtain approximation coefficients (ca), vertical coefficients (cv), horizontal coefficients (ch), and diagonal coefficients (cd). 6. Compute the Standard Deviation for ca, cv, and ch frequency bands separately. This forms 3 features. (a) original image (b) binarized image (c) thinned image (d) DCT image (e) DWT coefficients (shown in pink color) FIGURE 4.1. Text block feature extraction

4 Classification The k-nn classifier is used for recognition purpose. This method is well-known non-parametric classifier, where posterior probability is estimated from the frequency of nearest neighbours of the unknown pattern. The algorithm for k-nn is summarized as follows. Given an unknown feature x and a Euclidean distance measure, then: Out of N training vectors (each vector of size 4), identify the k nearest neighbors, regardless of class label k is choosen to be odd for two class problem. Out of these k samples, identify the number of vectors, k i, that belong to class i, i=1,2. Obviously,. Assign x to the class I with the maximum number of samples During the training phase, features are extracted from the training set by performing algorithm given in section 4.2. These features are input to k-nn classifier to form a knowledge base that is subsequently used to classify the test image. During test phase, the test image which is to be recognized is processed in a similar way as described in section 4.2. The classifier computes the Euclidean distance between the test feature vector with that of the stored features and identifies the k-nearest neighbour. Finally, the classifier assigns the test image to a class that has the minimum distance with voting majority. The corresponding script is declared as recognized script. 4.4 Experimental Results The proposed method is experimented on block level image database representing the handwritten documents of eight Indian scripts that includes English script. A total of 900 pre-processed block images each of size 512 x 512 pixels are used. The complete dataset is processed, as described in Chapter 2, to generate the ground truth for testing and evaluation of the algorithm. For performing experiments, we consider English script, Devanagari script, and a local language script for triscripts documents and English script and a local language script for bi-script documents. Samples of one script are input to the proposed system and performance is noted in terms of recognition accuracy. From the data set, 60 blocks are used for

5 55 training purpose and remaining 40 blocks are used as a test dataset. Identification of the test script is done using k-nn classifier for k=1,3,5, and 7. The results were found to be optimal for k=1. The results obtained for the bi-script and tri-script documents are tabulated in Table 1 and Table 2, respectively. The results clearly shows that the features computed using DCT and wavelets yielded better results. The results are promising for groups 1, 2, 3, 4, 6, 7 and 8 for bi-scripts. That is, recognition rate is higher for the documents containing Roman script and one of the Kannada, Malayalam, Punjabi, Tamil, Telugu, and Devanagari scripts. Similarly, the results are promising for groups 1, 2, 4, and 6 for tri-scripts. Table 4.1: Recognition results in % (Bi-script) Group Bi-scripts % 1 Kannada, English 98 2 Malayalam, English Punjabi, English Tamil, English Gujarati, English 90 6 Telugu, English 98 7 Devanagari, English Urdu, English 100 Table 4.2: Recognition results in % (Tri-script) Group Tri-scripts % 1 Kannada, English and Hindi 98 2 Malayalam, English and Hindi Punjabi, English and Hindi 93 4 Tamil, English and Hindi Gujarati, English and Hindi 90 6 Telugu, English and Hindi 99

6 56 Table 4.3: Confusion matrix for Tri-script 4.5 Summary A novel method has been described for script identification from handwritten documents at block level. The block extracted from the document is assumed to contain only characters from a single script. Features are extracted using discrete cosine transform and discrete wavelet transform. The k-nn classifier is used in recognition phase that yielded better results for k=1. Eight Indian scripts including Roman script are considered for performing experiments. Results are tabulated for biscript and tri-script groups. An average recognition of 98.06% is obtained for bi-script documents.

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