Droid Notes Notebook on a phone

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1 Droid Notes Notebook on a phone 28 Gurpreet Gill 1, Pratik Parookaran 2, Judson William 3, Samir Gulave 4 1 Department of Information Technology, KJs Educational Institute s Trinity College of Engineering & Research Abstract Mobile learning can create a revolution in the teaching field with the popularity of smart mobile phones and its applications. In this paper we propose a system which will help both the students and teachers by providing services that facilitate teaching as well as learning.the basic approach is by integrating various technologies like optical character recognition, cloud computing and text mining. Keywords - OCR, elearning, cloud, mlearning I. INTRODUCTION In today s fast paced world, PDA s seem to be the need of the hour. Keeping that in mind, replacing traditional paper notebooks with a PDA seems necessary. This upgrade also brings along with it the benefits of internet connectivity, allowing seamless sharing of data using cloud computing techniques. Also, effective search and retrieval of such data becomes possible using text mining techniques. Together with the integration of these technologies, a chatting facility for connected users is also provided thereby allowing a medium for communication. Finally, a centralized library is provided, allowing the participants to choose from a variety of available e-books. This paper describes our proposed system which combines these various techniques to provide a useful application which has the potential to replace the traditional paper notebook. The user can take notes down using on the PDA using OCR, once the note is complete, it can be uploaded to the cloud. The staff of the institution can use this proposed application to upload notes and e-books. The proposed application also includes features that allows the users to chat and initiate a thread to discuss a particular topic. A. Prior and Related Work The techniques included in our proposed system have been implemented to a certain extent individually. In our proposed system, we intend to combine all these techniques together to create a powerful application. A system of this sort has not been implemented in the past. The various features used in our proposed system are listed below with a brief description. 1) Optical Character Recognition-Optical Character recognition involves the reading of text from paper and translating the images into a form that can be manipulated by the computer. The capability of OCR systems is massive because they enable users to harness the power of computers to access printed documents. 2) Text Mining- Text mining involves analysing data within natural language text.it works by transposing words and phrases in unstructured data into numerical values followed by linking them to structured data within a database and using data mining techniques to analyse them. 3) Discussion Forum- This feature enables the users to interact with other conncected users. The users can also initiate a thread to discuss specific topics. Ths feature is introduced to enable users of the appllication to stay up to date with the current happenings and receive important notcices and announcements. 4) Cloud- In our proposed system we intend to use the cloud storage to store notes and e-books which can be retrieved later by the users of the application.

2 II. PROPOSED SYSTEM Fig 1. Flow Chart Fig 2. Architecture Diagram 29

3 Text Mining Query A query is entered to retrieve the appropriate content according to the user s requirements. Tokenization The entered sequence of string query is broken up into pieces like sentences and words by eliminating the whitespaces and punctuation marks. Stemming Stemming is a technique used to reduce the inflected words to their root words. The stem is similar to the root word and it is generally mapped to it. Parsing The body of the entered query is then extracted to which the regular expressions are applied upon which the further processing is done. Filtering The stop words such as and and the are filtered out to enable a higher level of accuracy. TF-IDF It stands for term frequency-inverse document frequency. It is a number that shows how important a word is to a document. In other words it s a weighting factor for information retrieval and text mining. Distance measure cosine similarity Fig 3. Text mining Flowchart In vector space model, cosine is used to measure the similarity between two vectors. The cosine of 0 degrees is 1 and it is less than 1 for any other angle. In text mining, each term is assigned a different dimension and a document is characterized by a vector where the value of each dimension links to the number of times the terms appear in the document. Thus cosine similarity gives a measure of how similar two documents could be in term of their subject matter. Sorting This is the technique wherein the documents are sorted in a prioritized manner and presented to the end user in that order. Here we are using sorting to sort the saved notes by the user for future reference. Also we are using the sorting technique for text mining purposes as in sorting according to relevance to the user query and presenting to the user in that order. Result Based on the above steps, an output is generated by the system. A. Abbreviations and Acronyms Tf - Term Frequency 30

4 Df Document Frequency Idf Inverse Document Frequency w tf-idf weight q i is the tf-idf weight of term i in the query d i is the tf-idf weight of term i in the document cos(q,d) is the cosine similarity of q and d or, equivalently, the cosine of the angle between q and d. B. Equations Score t q d idf t log10 N/df t w (1 log tf t d t, d ), (1 log tf t, d ) (1) log q q d d cos( q, d) q d q d N / df t V i V 2 qi i1 Optical Character Recognition 1 q d i i V i1 d 2 i (4) (3) (2) Grey Scaling This is the first step of Image Processing where in the image is converted to grey scale. This process is carried out because the image is in the original size of 24-bits of mixed colours. However we need to separate these colours. This step is known as RGB and the output of this process results in the generation of image which is grey in colour. For this process we are using the grey scaling algorithm which is as follows. First we traverse through the entire image array and read the individual pixel colour value. Fig 4. OCR Flowchart Split the colour value into Red, Green and Blue components of 8-bits each [1]. Now we calculate the greyscale component which is of 8-bits for the values of RGB using the conversion formula. Then finally we combine these values and store them as an input somewhere in the image. The algorithm is as follows. Thresholding Thresholding is the simplest method of image segmentation. From grey scale image thresholding can be used to create binary image which is the image in pure black and white colours. It is usually used for feature extraction where the required feature of the image is converted into white and the background is converted into black or vice versa. The algorithm takes the image obtained from the grey scaling process and then operates on it. Here we have to set a threshold which is then used by the system to separate the pixels of the image. 31

5 Thinning Thinning is the process where the image is blurred and then thinned. Blurring an image reduces the sharpening effect hence making the detection process more accurate. Hence thinning uses blurring as for this process it makes the alphabets of the OCR more visible to the system to match. Cropping The image is cropped according to the predefined size. The cropping process is the same as for others. This process is used to separate the image or alphabets according to size. Scaling This process is used to match the image of the alphabet to the one stored in the database. The cropping process separates the alphabets and then after scaling the applied to get the characters to a predefined size as the ones stored in the database. Feature Extraction (Template generation) This process is used to generate the templates of the alphabets and then these templates are matched to the alphabets or characters which are stored in the database. Feature Matching (Template matching) Once the templates are generated from the feature extraction process then the images comes to this phase of feature matching where the templates generated are matched with the pre-stored templates in the database and the characters which match the template generated are then given as output. Acknowledgment The authors acknowledge the valuable help provided by Prof. Samir Gulave, Prof.Sneha Tirth, Prof.Bilkis Chandargi. Thanks are also due to the staff members of the Department of Information Technology, Trinity College of Engineering and Research, Pune for their valuable time, support, comments, suggestions and guidance. References [1] Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models Salvador Espan a- Boquera, Maria Jose Castro- Bleda, Jorge Gorbe-Moya, and Francisco Zamora-Martinez. [2] Mobile Camera Based Text Detection and Translation Derek Ma,Qiuhau LinTong Zhang [3] Document Clustering for Forensic Analysis: An Approach for Improving Computer Inspection, da Cruz Nassif, L.F. Brazilian Fed. Police Dept., Sao Paulo, Brazil Hruschka, E.R., 09 October [4] F. Zamora-Martı nez, M.J. Castro-Bleda, S. Espan a-boquera, and J.Gorbe-Moya, Improving Isolated Handwritten Word Recognition Using a Specialized Classifier for Short Words, Current Topics in Artificial Intelligence, pp , [5] A. Graves, M. Liwicki, S. Ferna ndez, R. Bertolami, H. Bunke, and J. Schmidhuber, A Novel Connectionist System for Unconstrained Handwriting Recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp , May [6] Y. Bengio, Learning Deep Architectures for AI, Foundations and Trends in Machine Learning, vol. 2, no. 1,

6 [7] H. Fujisawa, Forty Years of Research in Character and Document Recognition An Industrial Perspective, Pattern Recognition,vol. 41, no. 8, pp , [8] J. Gorbe-Moya, S. Espan a-boquera, F. Zamora-Martı nez, and M.J.Castro-Bleda, Handwritten Text Normalization by Using Local Extrema Classification, Proc. Eighth Int l Workshop Pattern Recognition in Information Systems, pp , [9] R. Gemellovo, F. Mana, and D. Albesano, Hybrid HMM/Neural Network Based Speech Recognition in Loquendo ASR, pdf, [10] A. Graves, S. Fernandez, M. Liwicki, H. Bunke, and J. Schmidhuber, Unconstrained Online Handwriting Recognition with Recurrent Neural Networks, Advances in Neural Information Processing Systems, vol. 20, pp , MIT Press,