Investigation on Algorithm for Handwritten Gujarati OCR Ph.D
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Investigation on Algorithm for Handwritten Gujarati OCR Ph.D. Synopsis Submitted To Gujarat Technological University For the Degree of Doctor of Philosophy in Electronics and Communication Engineering By Mikita R. Gandhi Enrollment No: 149997111007 Supervisor: Co-Supervisor: Dr. Vishvjit Thakar Dr. Hetal N. Patel Associate Professor and Head Professor & Head Information and Communication Technology, Electronics and Communication Department, Sankalchand Patel University, A.D.Patel Institute of Technology, Visnagar. New V. V. Nagar. Table of Contents 1. Title of the Thesis and Abstract............................................................................. 1 2. Brief description on the state of the art of the research topic................................ 2 3. Objective and Scope of the work........................................................................... 4 4. Original contribution by the thesis......................................................................... 4 5. Methodology of Research, Results / Comparisons................................................ 4 6. Achievements with respect to objectives............................................................... 17 7. Conclusions............................................................................................................ 18 8. List of publication arising from the thesis............................................................. 19 9. References.............................................................................................................. 20 1. Title of the Thesis and Abstract 1.1 Title of the Thesis: Investigation on Algorithm for Handwritten Gujarati OCR 1.2 Abstract: Optical Character Recognition is getting much more attention because by this the computer learns and recognizes the regional languages pretty well and if it successes, then it opens a whole new world of endless possibility.The machine printed characters are accurately recognizable which has solved many problems and hence commercialized in routine use but the recognition of hand written characters are very difficult and methods of recognition of hand written documents is still a subject of active research. There is no common algorithm is possible for all Indian language, because each Indian language has its own features and restrictions. In Gujarat state, Gujarati is the commercial language and most of the communication in Government office, schools and private sectors is done in Gujarati. Handwritten Gujarati OCR system was developed for handwritten amount on cheque, automatic reading of marks from answer sheet and a learning application for education system. The research work is mainly focused on implementation of robust algorithm for Handwritten Gujarati OCR. The KNN and SVM classifiers were used on different feature extraction methods like pixel count ratio, object gradient; geometry, profile, local binary pattern, ceter-symmetric local binary pattern and wavelet transform methods. Furthermore hybrid feature extraction methods were used for increase the performance of character recognition. The other novel approach of automated features extracted was implemented using Deep learning. The extracted features were given to SVM for handwritten character classification. For increasing recognition rate of characters, pretrained Deep Neural network (Alexnet) has been used and implemented three different application: Handwritten Guajarati Numeral to speech conversion, character to speech conversion and Automatic Handwritten Marks Recognition. KNN, SVM and Deep Neural Networks gives recognition accuracy of 98.14%,98.72% and 99.30% for Numeral, 92.37%, 92.21% and 97.65% for characters and 92.64%, 92.93% and 97.73% for combining Numerals and characters respectively. 1 2. Brief descriptions on the state of the art of the research topic As the world move closer to the concept of the “paperless office,” more and more communication and storage of documents is performed digitally. Documents and files that were once stored physically on paper are now being converted into electronic form in order to facilitate quicker additions, searches, and modifications and also doing this, life of such documents are prolonged. The advances in character recognition were limited to the extraction of English language character for both digital and handwritten. The character recognition of Indian languages can help authors, novelist, and many people to recognize the Indian characters and even to extract old heritage documents. The research work is approximately negligible for handwritten character recognition in general for Indian languages and Gujarati language in particular. In Gujarat State, all Government agency documents are written in Gujarati language. The software is available for printed Gujarati OCR but recognition of handwritten character is still changing exertion. Basic block diagram of OCR system is shown in figure 2.1. There are five major stages are like preprocessing, segmentation, representation, training and recognition and post processing. Figure 2.1 Basic block diagram Preprocessing is required to make the raw data usable in the descriptive stages of character analysis like smoothing, sharpening, binarize the image, remove background and extracting the required information. Segmentation converts the document into separate character by first segment the lines, then line segments the words and from words to individual characters which is used by classifier. In representation stage, the set of features are extracted to distinguished one class of the images from other class. KNN, SVM, Neural Network, Deep Learning like classifier are used for training and recognition. The Gujarati OCR worked was initiated by Sameer Antani et.al.[1] on printed Gujarati Script.KNN and hamming distance classifier was applied on 15 characters; 30 samples for 2 each character and got 67 % and 41.33% accuracy respectively. Using template matching and wavelet transform coefficients [2], Shah S. K et.al. attained 72.30 % accuracy for printed Gujarati OCR, Ankit K. Sharma et.al. [3] worked on zoning method and using multilayer feed forward neural network classifier achieved 95.92% accuracy for handwritten Gujarati Numerals and Archna vyas et.al.[4] got 96.99% accuracy using KNN. Using hybrid feature space method and SVM classifier A. Desai [5] has recognize forty handwritten Gujarati characters with 86.66% accuracy. The Zonal Boundary was successfully detected [6] by Jignesh Dholakia et.al. using zoning method. Swital J. Macwan et al [7] has applied discrete wavelet transform method on Gujarati Handwritten and got 89.46% accuracy. V. A. Naik et al have used different structural and statistical features for recognition of handwritten numerals and acquired 95% accuracy [8] and Dinesh Satange et al. obtained 90% accuracy using Multi Layer Perception[9]. Ashutosh Aggarwal et.al. [10]has worked on gradient based feature extraction and SVM classifier for Hindi handwritten character recognition. LBP features are used for Bangla digits recognition in 2015 [11] and achieved 96.7% accuracy using KNN classifier; the same LBP feature applied on Persian/Arabic handwritten digit recognition[12]. Sekhar Mandal et al [13] proposed algorithm for machine-printed character recognition in Bangla language using two dimensional wavelet transform and gradient information. Saleem Pasha et al have solved problem of handwritten recognition for Kannada language using statistical featuresand wavelet transform [14]. Two stage CNN network was used by Shibaprasad Sen et al [15] for online Bengali handwritten character recognition and gain 99.40% accuracy. Akm Ashiquzzaman et al worked on 10 different layer of CNN architecture for Arabic handwritten digit and achieved 97.4% accuracy [16]. Chaouki Boufenar et al shown the three different approach of Deep learning methods for handwritten Arabic character recognition: i) scratch approach; (ii) transfer learning approach and (iii) fine-tuning approach [17]. Table 2.1 shows the Gujarati Numerals and Characters used for research work. Numerals ૦ ૧ ૨ ૩ ૪ ૫ ૬ ૭ ૮ ૯ Characters ક ખ ગ ઘ ચ છ જ ઝ ટ ઠ ડ ઢ ણ ત થ દ ધ ન પ ફ બ ભ મ ય ર લ વ શ ષ સ હ ળ ક્ષ જ્ઞ શ્ર અ ઋ ઈ ઉ ઊ Table 2.1 Gujarati Numerals and Characters 3 3. Objective and Scope of work 3.1 Objective: To develop an algorithm for handwritten Gujarati OCR features to recognize numerals and Character. To design an Optical Character Recognition system for handwritten Gujarati Numerals. To design an Optical Character Recognition system for handwritten Gujarati Characters. To design an Optical Character Recognition system for combined handwritten Gujarati Numerals and Characters. 3.2 Scope of work: The research work is useful to automatic detection of amount written in Gujarati on bank cheque, marks written on answer sheet and in Gujarati numeral and characters learning application. Handwritten Gujarati numeral and characters to speech conversation The implemented algorithms can be useful for recognition of Gujarati text modifier. 4. Original contribution by the thesis Develop different feature extraction methods along with creation of database for Gujarati Handwritten numerals and characters. Three different classification methods: KNN, SVM and Deep learning was used for recognition Hybrid features with the above listed three classification methods. Transfer learning approach of Deep learning is used for better accuracy. Three applications are developed: Gujarati handwritten Numeral to speech conversion Gujarati handwritten