Assisting Visually Challenged Person in the Library Environment

Nowadays, there is lot of assistive technologies to support visually impaired people. Among them computer vision based methods provide a feasible solution. An indoor environment provides challenges like object recognition, character and scene recognition. It is important to understand that people need to know information about things; places and they may feel insecure in places like their working places, shopping mall because of their challenges in vision. It is essential that a technology based solution can be provided to support the people so that they can be guided along in their pathways, rooms, shopping malls and they can also access things in their living environment. In this paper a model is proposed for detecting text from library book shelf scene video and informing the user about book name through audio to assist visually impaired people in accessing their book which is kept on shelf in library. Key frames are extracted using PSNR and Edge Change Ratio method. Text on the key frame is detected and localized using MSER and Projection profiles. CNN is used to recognize characters from the localized text. This paper gives an outline of different techniques which are combined to extract key frames, localize and recognize text from natural library scenes.

[1]  T. Senthil Kumar,et al.  Discrimination and Detection of Face and Non-face Using Multilayer Feedforward Perceptron , 2016 .

[2]  Kannan Balakrishnan,et al.  Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier , 2012 .

[3]  Jean-Michel Morel,et al.  Non-Local Means Denoising , 2011, Image Process. Line.

[4]  Huayong Liu,et al.  Key frame extraction based on improved hierarchical clustering algorithm , 2014, 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[5]  Mahmood Fathy,et al.  Key-frame extraction and video summarization using QR-Decomposition , 2010, 6th International Conference on Digital Content, Multimedia Technology and its Applications.

[6]  R MamathaH,et al.  Morphological Operations and Projection Profiles based Segmentation of Handwritten Kannada Document , 2012 .

[7]  Anand Singh Jalal,et al.  A framework for key frame extraction from surveillance video , 2014, 2014 International Conference on Computer and Communication Technology (ICCCT).

[8]  Kaizhu Huang,et al.  Robust Text Detection in Natural Scene Images , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Majid Mirmehdi,et al.  Real-Time Detection and Recognition of Road Traffic Signs , 2012, IEEE Transactions on Intelligent Transportation Systems.

[10]  Chirag I. Patel,et al.  Handwritten Character Recognition using Neural Network , 2011 .

[11]  Yonatan Wexler,et al.  Detecting text in natural scenes with stroke width transform , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Senthil Kumar Thangavel,et al.  Hidden object detection for classification of threat , 2017, 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS).

[13]  Sharanjeet Kaur Sandhu,et al.  Summarizing Videos by Key frame extraction using SSIM and other Visual Features , 2015, ICCCT '15.

[14]  Mohamed Cheriet,et al.  Tensor representation learning based image patch analysis for text identification and recognition , 2015, Pattern Recognit..