Word Level Handwritten and Printed Text Separation Based on Shape Features

International Journal of Emerging Technology and Advanced Engineering

590

Word Level Handwritten and Printed Text Separation

Based on Shape Features

Upasana Patil

, Masarath Begum

1,2_{Department of Computer Science, GND Engineering College, Bidar, India} 1_{[email protected]}

2_{[email protected]}

Abstract— In this paper, we present a method for discriminating handwritten and printed text from document images based on shape features. The separation of handwritten and printed text from document image is essential to optimize the OCR accuracy and to activate an appropriate OCR engine. It leads to reduce the search space of the OCR and it also facilitates the retrieval of Handwritten and Printed text from document images. We have used IAM

dataset 3.0 and with morphological transformations

segmented 74 pages and obtained 10768 words and 2000 were used for experimentation and achieved average accuracy of 98.57% with only seven features. The proposed method is simple, have promising discrimination accuracy and less time complexity as compared to [10].

Keywords—Document Image Analysis, OCR, Shape Features Handwritten and Printed Text.

I. INTRODUCTION

Integration of new technologies and inventions leads us towards the achievement of paperless office and paperless society. Document image analysis is one of the import steps in automating the offices. Every activity of the office involves papers, which are in the form of petitions, application forms, reports, letters and accounts. In most of the situations we come across with numerous documents presenting a mixture of handwritten and printed text. For example, railway reservation forms, bank cheques, memorandums etc. Often we notice that interlacing of handwritten and printed text is at word level, line level and paragraph level. The recognition of such documents is a challenging task for OCR designers. To optimize the OCR accuracy, separation of handwritten and printed text from such documents is very essential prior to activation of the OCR engine. Handwritten and printed text separation leads to reduce the search space of the OCR and it also facilitates the retrieval of Handwritten and Printed text documents. Thus, the problem of Automatic Discrimination of between Handwritten and Printed text from Document Images may be addresses in three different cases as they classified as

Paragraph Level Separation, Line Level Separation and Word Level Separation. In this paper, word level handwritten and printed text separation is carried out.

In Section 2, we present survey of literature. The details of dataset used for experimentation is presented in Section 3. In Section 4, feature extraction methodology is given and Section 5 and 6 describes the classification of text words and results respectively. Conclusion is dawn in Section 7.

II. LITERATURE SURVEY

There exists some research publications on

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591

J Koyama et al.[9] introduced a scheme for distinction between handwritten and machine printed characters named as (SDLFD) spectrum domain local fluctuation detection method. They transform local region of document into frequency domain and feed feature values to optimize multilayer perceptron to get likelihood of handwriting. Lincolon et al. [10] developed a scheme to discrimination between printed and handwritten text into documents. They extracted features deviation of width, height and areas, density, vertical projection variance, major horizontal projection difference, pixels distribution, bottom line, black pixels of each line, vertical edges, and Major vertical edge, mining rules used for classification.

III. DATASET

IAM dataset 3.0 [32] is considered for experimentation. Word level dataset is created using 74 pages containing handwritten and printed text. These pages are segmented using simple morphological operations and obtained 10768 word images. Each 1000 text words of printed and handwritten text are used for training and testing. The sample images used for experimentation are shown in Figure. 1, 2, 3, 4 and 5

Figure 1 Input image of IAM database

Fig. 2 Binarized image of IAM database Fig. 3 Image after line

removal

Figure 4 Sample segmented handwritten text words

Figure 5 Sample segmented printed text words

IV. FEATURE EXTRACTION

Documents are binarized using N. Otsu’s A Threshold Selection Method from Gray-Level Histograms [22].Noise is removed using basic morphological operations. Based on connected component analysis long lines are removed from the document images. Before designing a feature extractor, shape of the connected components of the handwritten and printed text words are keenly observed and noticed some differences in roundness, stroke orientation, compactness and aspect ratios of each component and extracted these visual discriminating shape features of the connected components of the word by using simple shape features like circularity, compactness, area, major axis, minor axis and aspect ratio. The following features are calculated on every connected component of the word as discussed below: 1. Area: It is defined as the actual number of pixels in the

region

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3. Form factor=

2 4 Perimeter

xArea



4. Major Axis: It is defined as the length (in pixels) of the major axis of the ellipse that has the same normalized second central moments as the region

5. Minor axis: It is defined as the length (in pixels) of the minor axis of the ellipse that has the same normalized second central moments as the region.

6. Roundness=

2 4

XMajorAxis xArea



7. Compactness=

MajorAxis xArea)/ 4

(

Derived a feature vector of seven features by computing the mean of the above-mentioned features as defined below:

1. F1=

N Area

N

i i



1

2. F2=

N Perimeter

N

i

i



1

3. F3=

N factor Form

N

i

i



 

1

4. F4=

N MajorAxis

N

i

i



1

5. F5=

N MinorAxis

N

i

i



1

6. F6=

N Roundness

N

i

i



1

7. F7=

N s Compactnes

N

i

i



1 _{, where N is the number of}

connected components of a word

V. CLASSIFICATION OF TEXT WORDS

K-nearest neighbour is a supervised learning algorithm. It is based on minimum distance (Euclidian distance metric is used) from the query instance to the training samples to determine the k- nearest neighbours. After determining the k nearest neighbours, we take simple majority of these k-nearest neighbours to be the prediction of the query instance. The experiment is carried out by varying the number of neighbours (K= 3, 5, 7) and the performance of the algorithm is optimal when K = 1.

Proposed algorithm

Input: Input gray image of IAM database

Output: Classified handwritten and printed text words

1. Input gray scale image of IAM database

2. Pre-process the input document image i.e. binarization

using Otsu’s method, and remove speckles using morphological opening.

3. Apply connected component’s processing for line

removal

4. Apply horizontal dilation with line structuring element to join the connected components of a word

5. Crop and fix the bounding box on each segmented word and save into a folder

6. Extract the shape features of connected components of

a word as defined above

7. Compute the average of the shape features as discussed

above and form a seven feature vector

8. Classify the handwritten and printed text words using KNN classifier with K=1 and two fold cross validation.

9. Stop

VI. RESULTS AND DISCUSSION

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TABLE1

CLASSIFICATION AVERAGE ACCURACY IN PERCENTAGE WITH KNN AND

K=1

Text Average

Handwritten 97.14%

Printed 100.00%

Average 98.57%

TABLE 2

CONFUSION MATRIX

Text Handwritten Printed

Handwritten 971 29

Printed 0 1000

TABLE 3

COMPARATIVE ANALYSIS WITHOUT CROSS VALIDATION

Lincoln Feria et

al.[10]

Proposed

Method

Dataset Used IAM DB3.0 IAM DB3.0

Classification Word Level Word Level

No.of Features

Used

09 07

Average accuracy 97.82% 98.57%

TABLE 4

COMPARATIVE ANALYSIS WITH CROSS VALIDATION

Lincoln Feria et al.[10] Proposed

Method

Dataset Used IAM DB3.0 IAM DB3.0

Classification Word Level Word Level

No.of Features Used 09 07

Cross Validation K-fold Cross V with k=10 K-fold Cross V

with k=10

Average accuracy 100% 100%

From table 1 and 2 it is clear that the miss classification of handwritten as printed is 2.9% whereas in printed text there is no misclassification. Table 3 and 4 reveals that the proposed method is simple, efficient and having less time complexity over [10]. The method presented by [10] employed projection profile features which have high time complexity.

VII. ACKNOWLEDGMENT

We are very grateful to the reviewers for their valuable comments. We also extend our gratitude to Dr. Mallikarjun Hangarge, Srikanth Doddamani and Rajmohan Pardeshi of Karnatak College, Bidar for giving their valuable suggestions during this work.s

VIII. CONCLUSION

In this work, we present an algorithm for automatic discrimination between handwritten and printed text using simple shape features. This algorithm is robust to the noise and has less time complexity as compared to [10]. Lincoln Feria et al. has used 9 features and achieved 97.82% accuracy of classification without cross validation; however, proposed method achieved 98.57% with only seven features. In future, we will extend this method by reducing the feature dimension by feature selection process to obtain 100% classification accuracy at character level. The classification of Handwritten and Printed characters is essential particularly with the documents available in India. As India is a multilingual country, and hence we come across with various documents which contain the combination of different scripts at character level. Hence, the extension of the proposed method to character level separation is justifiable.

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