Science, Technology and Development ISSN : 0950-0707

RECOGNIZING HANDWRITTEN CHARACTERS USING MULTI- – A COMPARATIVE STUDY

1Syeda Maherunnisa, 2Mohammad Sanaullah Qaseem 1PG scholar, Department of CSE, Nawab Shah Alam Khan College of Engineering and Technology, Hyderabad. 2Professor and Head, Department of CSE, NSAKCET, Hyderabad

ABSTRACT Handwritten Character Recognition (HCR) is an intriguing area for applying . It is a popular belief that the doctor’s handwriting is not so legible. But when it comes to the other languages especially the regional languages it becomes more difficult to interpret the scribbled nature of various types of handwritten letters, scriptures, postcards, diary entries, paper post it messages to name a few. Its not a difficult job for a literate human to recognize the characters or ‘lipi’of a language, but when it comes to using Artificial intelligence as a cutting-edge technology, interpretation becomes easy. The handwritten text is partitioned based on its understanding, into three levels viz, directly understandable, moderately understandable and troublesome. A Handwritten Character Recognition method is proposed which is totally dependent on Artificial Intelligence, using the Multi-Layer Perceptron (MLP) calculations. The manually written textual styles utilized here are multi-content, which comprises of pictorial datasets of Bangla Textual Style, Latin Style and MNIST Style. Different pictures utilized in this trial are preprocessed using the OCR. The originally image set was the MNIST, Latin and Bangla text style set in a test to quantify the adequacy of this exploration. The trial and the outcomes show more precise consequences of this framework contrasted with the other methodologies.

Keywords: Handwritten Character Recognition, Multi-Layer Perceptron, OCR, Bangla data, Latin data, MNIST

1. INTRODUCTION Manually Written Character Recognition is a difficult subject in the field of artificial intelligence. This technique encourages the interpretation of various types of scribbled texts of various types of handwritten letters, scriptures, postcards, diary entries, paper post it messages etc. The complexity based in understanding it is isolated into three levels, basic (Fig 1), moderate (Fig 2) and troublesome (Fig 3) as appeared in the figure’s underneath.

Fig. 2. A Moderate Handwriting

Fig. 1. A Basic Handwriting

Fig. 3. A Troublesome Handwriting

In this paper, we examine a proper technique to build up another calculation for recognition using artificial insight dependent on the applied structure of research. The test and the outcomes show better results of

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this framework contrasted with the Support Vector Machine 2. LITERATURE REVIEW (SVM) calculation. The Data Set models viz Bangla, Latin and Earlier works by researchers [1] is delivers a group of MNIST is given in the Fig. 4, Fig.5, and Fig.6 separately. character fragments (fraglets). Utilizing a codebook of Bangla is the second most mainstream language in India. It fraglets from a free set, the likelihood appropriation of consists of 50 basic alphabets which includes 11 vowels and fraglet shapes was figured for an autonomous set. Another 39 consonants. Even though its a widely used language, study [2] discusses the various informational collections there has not been much research conducted on the utilizing the field of archive examination, to show that new handwriting recognition of this language, compared to techniques can possibly outflank conventional strategies English. Latin is utilized for Identifying measurable names applied to diagram portrayals. In [3] the dissemination of and utilizing the firemaker informational index as manually samples is seen by the closest neighbor investigation and written notes. MNIST informational index is a digitized network examination, the two of which don't make any picture that is scaled to the norm and fixated on a manually estimate and subsequently don't degenerate the subtleties of written static size. The MNIST database (Modified the circulation. Vapnik Etal in their study suggest that the National Institute of Standards and Technology database) is Support Vector Machine (SVM) calculation was applied to an enormous database of handwritten digits which are used numerous sorts of acknowledgment issues. Straight SVM usually for training various image processing systems. The valuable for arrangement issues and will locate the best database is also widely used for training and testing in the hyperplanning, with the greatest separation at the field of . preparation guide close toward the hyperplane. Non-Linear SVM issues are for various gatherings which it does by making and joining numerous parallel identifiers.

3. PROPOSED METHODOLOGY The proposed handwritten character recognition methodology has been implemented using Optical Character Recognition (OCR). The change of pictures of printed, transcribed or composed content is finished by Optical Character Reader (OCR). The Conversion of

Fig. 4. Bangla Data Set pictures is done from a checked archive, a photograph of the record. Broadly utilized as a type of data whether it is identification information, it is a typical technique for digitalizing printed messages so they can be electronically altered, looked, put away more minimally, showed on-line, and utilized in machine interpretation, (extricated) text-to-discourse, key information and text mining. OCR is a tremendous field of examination in design acknowledgment, artificial knowledge and PC vision. It is a process of classifying optical patterns with respect to alphanumeric or other Fig. 5 Latin Data Set characters. Optical character recognition process includes segmentation, and classification. Text capture converts Analog text-based resources to digital text resources. And then these converted resources can be used in several ways like searchable text in indexes so as to identify documents or images.

1. Preprocessing 2. Segmentation Fig. 6. MNIST Data Set 3. Feature Extraction 4. Class Prediction

As the first stage of text capture a scanned image of a page is taken. And this scanned copy will form basis for all other

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stages. The very next stage involves implementation of Highlight Extraction is the most basic aspect of this technology Optical Character Recognition for converting theory, consequently expounding some more subtleties. text content into machine understandable or readable The eight-neighbor contiguous philosophy is utilized for format. OCR analysis takes the input as digital image which extraction data on the limit of a manually written character. is printed or handwritten and converts it to machine Limit of the character is examined and identified by readable digital text format using the checking it in the parallel picture design. Clockwise bearing Learning Algorithm developed is pertaining to the stages as inquiry is completed. The Two pixels are associated on the described below: off chance that they are neighbors and their grayscale levels Step 1: Initializes the Network, with all the weights and set are in accordance with the predefined exactness of to random numbers between -1 to +1. closeness. The informational collection of the apparent Step 2: Presents the First Training Patter and Obtain the multitude of pixels associated P is called part containing P. Output. This is appeared in Fig.7. Step 3: Compares the Network Output with the Target Output. Step 4: Propagates the error backwards. We need to correct the weights of the output layer and then Correct the input weights. Step 5: Takes the average difference between the target and the output vector to calculate the error. Step 6: Repeats Step 2 to 5 for each pattern in the training set. (one complete epoch).

Step 7: Shuffle the training set randomly. Fig. 7. Pixel P with 8-Neighbors Step 8: Repeat Step 2 for a set number of epochs, or until At the point when a white pixel is identified, it checks the error ceases to change. another new white pixel and afterward that pixel again checks another white pixel, etc the way toward finding the The OCR then processes this digital image after performing white pixels proceed. The following follows on persistently the MLP algorithm into small components for analysis of till the limit is naturally got. The limit is reserved as finding text or word or character blocks. And again, the appeared in Fig. 8. character blocks are further broken into components and are compared with dictionary of characters.

Preprocessing Information Preprocessing alludes to the means which are applied to make information more appropriate for information mining. The means utilized in Data Preprocessing are generally of two classifications: • Selecting information objects and characteristics for the Fig. 8. Images showing the boundary examination.

• Creating/changing the traits. The Inner Boundary Tracing is finished utilizing the

Inner Boundary Tracing Algorithm. Right off the bat the Segmentation picture from upper left until a pixel of another area is found Picture division is the way toward separating is looked, this pixel P1, at that point has least section advanced picture into various sections. The point of estimation of the apparent multitude of pixels of that locale division is to rearrange, change the picture into something having a column esteem. At that point search the 3x3 more significant and simpler to examine. Picture division is neighborhood of the flow pixel in hostile to clockwise commonly used to find articles and limits which are bearing. The if the flow limit component P1 is same to the typically lines, bends and so on in pictures. second limit component P2, and if the past fringe

component Pn-1 is same as P1, stop, else then keep on Feature Extraction looking through the 3x3 neighborhood of the current pixel. Feature Extraction in Image handling for handwriting The internal limit is distinguished and spoken to by pixels recognition begins with estimated information and P1 … .. Pn-2. manufactures inferred values, which are expected to be educational, which encourages the ensuing learning and speculation steps, prompting better human understandings.

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Fig. 9 Inner Boundary Tracing:

9(a) Direction Notation, 4-connectivity; Fig. 10. Outer Boundary Tracing. The Dots denoted 9(b) It depicts the 8-boundary; the out-boundary elements. 9(c) This is the Pixel Neighborhood search sequence in 4- connectivity; Class Prediction 9(d) and 9(e) These are the search sequences in 8- Yield of the Algorithm is alluded as "Expectation", connectivity; after it has been prepared on verifiable dataset and applied 9(f) Boundary Tracing in 8-connectivity with the dashed to new information when attempting to estimate the lines showing pixels tested during the border tracing. probability of a specific result. The calculation creates plausible qualities for an obscure variable in the new The Outer Boundary Tracing is finished by utilizing the information for each record, permitting the model Outer Boundary Tracing Algorithm. Here right off the bat developer to recognize what that worth will in all the internal district of the limit in 4-availability is done work likelihood. it is obviously got. At that point all the non-locale pixels from the external limit which are pertinent that were tried 4. EXPERIMENTAL INVESTIGATIONS during the inquiry cycle is done; on the off chance that a In view of the different examination measures depicted few pixels were tried more than once, at that point they are the productivity of the calculation and strategies introduced recorded more than once in the external limit list which is were tried and the exactness of the proposed calculation is appeared in Fig. 10. determined. This paper manages perceiving transcribed characters from 3 informational indexes of characters from Once the boundaries are earmarked and found. The different analyst’s dependent on artificial knowledge. The Fourier Descriptors are involved in finding the Discrete difficulties of these characters are comparative. Bangla is Fourier coefficients through equations (1) and (2) as given second most famous language in India and comprises of 45 below. classes and 4,627 classes for the preparation set and 900 models in the test suite. Latin transcribed characters were in Dutch with 251 journalists who had 37,616 characters ie., 26,329 practice models and 11,287 examples. MNIST informational index was a bigger information bases of numbers which is frequently utilized for preparing picture handling frameworks. The underneath Table 1. sums up the Once the Discrete Fourier Coefficients are derived, they are Handwritten Data Series. computed by eliminating the size of the character and thus the Feature is extracted.

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Screenshot 1 Table 1: Summary of the Handwritten Data Series for Bangla, Latin and MNIST

The Handwritten Character Recognition system to recognize or show measured values close to actual values was Evaluated. To Calculate the accuracy or precision the following equation was used. This paper compares the methodology used by Support Vector Machine; k Nearest Neighbor and Multi Layered Perceptron (MLP) on the above Data., Bangla, Latin and MNIST. The results of the Screenshot 2 classification accuracy is shown in the Table 2.

Data Set Accuracy Rate MLP kNN SVM Bangla 91.05 85.60 90.5 Latin 94.58 93.31 93.8 MNIST 94.8 94.11 94.6

Table 2. Accuracy Rates using 3 Handwritten Character Sets from the Comparison (%) of SVM, kNN and MLP Screenshot 3 The accuracy of the handwriting recognition on MNIST

data set with MLP was Bangla set up to 91.05 %, Latin set up to 94.58 %, and MNIST set up to 94.8 %. Therefore, the MLP Character Recognition can be recognized as the better alternative.

5. IMPLEMENTATION & RESULTS

Python programming language was used to develop an application, the Server process is maintained by using the TensorFlow. It has a huge library we can import the library for performing OCR task. A use of python includes Screenshot 4 analysis, algorithm development, computation and much more. The following screen shots Screenshots 1 to 5, showcase the implementation of the recognition of handwritten language characters successfully.

Screenshot 5

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6. CONCLUSION [6] Chumuang, N. and Mahasak, K., “The Most Intelligence This study aims to devise a new progressed algorithm for self-character recognition on palm leaf calculation for manually handwritten frameworks. This manuscript” Far East Journal of Mathematical Sciences examination utilizes a lot of in sequential order pictures in (FJMS) volume 98, Issue 3, pp. 333-345, October 2015. Bangla, Latin and MNIST textual styles, which are [7] Chamchong, R., Fung, C., and Wong, K. W. (2010). regularly utilized in the current case for character Comparing total binarisation techniques for the processing acknowledgment. During the various stages the pictures of self-ancient manuscripts. In Nakatsu, R., Tosa, N., utilized in this analysis are separated into arrangement of Naghdy, F., Wong, K. W., and Codognet, P., editors, The pictures sets. The originally set was the Bangla, Latin and Cultural Computing, volume 312 of IFIP Advances in MNIST textual style set in a test to check the exactness of Future Information and Communication Technology, pages this examination. The test picture is a very much shaped 55–64. Springer Berlin Heidelberg. textual style between the content and the foundation where the foundation of the image is white. [8] Van der Zant, T., Schomaker, L. R. B., and Haak, K. This analysis and the outcomes show improved outputs of (2008). Handwritten word spotting using most recent this framework compared with the Multi-Layer Perceptron biologically inspired features. Pattern Self Analysis and (MLP) algorithm performance. The comparative analysis Machine Intelligence, IEEE Transactions on, 30(11):1945– shows that the accuracy of the Bangla set up to 91.05 %, 1957. Latin set up to 94.58 %, and MNIST set up to 94.8 % which [9] N. Chumuang and M. Ketcham, "Model for indicates that the performance of Multi-Layer Perceptron Handwritten Recognition Based on Artificial Intelligence," (MLP) is better than is counterparts. 2018 International Joint Symposium on Artificial 7. FUTURE ENHANCEMENTS Intelligence and Natural Language Processing (iSAI-NLP), Pattaya, Thailand, 2018, pp. 1-5, doi: 10.1109/iSAI- In future there is extent of expanding the quantity of NLP.2018.8692958. the shrouded layers as much as we need i.e., the Input Layer and the Output Layer will be normal. Also, the model can be made more unpredictable by utilizing different number of centers concealed layers for numerous preparing of the picture information type for exact outcomes.

REFERENCES [1] Schomaker, L. R. B., Franke, K., and Bulacu, M. (2007). Using codebooks of fragmented connected-component contours in forensic and historic writer identification. the Letters, 28(6):719–727. Pattern Recognition in Cultural Heritage and Medical Applications. [2] Bunke, H. and Riesen, K. (2011). Recent the advances in graph-based pattern self-recognition with applications in document analysis. Pattern Recognition, 44(5):1057–1067. [3] Liwicki, M., Bunke, H., Pittman, J., and Knerr, S. (2011). Combining the diverse systems for handwritten text lineself recognition. Machine Vision and Applications, 22(1):39–51. [4] Uchida, S., Ishida, R., Yoshida, A., Cai, W., and Feng, Y. (2012). Character of the image patterns as big data. In Frontiers in Handwriting Self Recognition (ICFHR), The 13th International Conference on, pages 479–484. [5] Khakham, P., Chumuang, N., and Ketcham, M., "Isan Dhamma Handwritten Characters Recognition Self System by Using the Functional Trees Classifier," 2015 11th International Conference on SignalImage Technology & Internet-Based Systems (SITIS), Bangkok, 2015, pp. 606- 612. doi: 10.1109/SITIS.2015.68

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