Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

An approach for text detection and reading of product label for blind persons

724 views

Published on

An approach for text detection and reading of product label for blind persons

Published in: Engineering
  • Be the first to comment

  • Be the first to like this

An approach for text detection and reading of product label for blind persons

  1. 1. Abstract Of the 314 million visually impaired people worldwide, 45 million are blind. Even in a developed country like the U.S., the 2008 National Health Interview Survey reported that an estimated 25.2 million adult Americans (over 8%) are blind or visually impaired. In our society to survive reading is obviously essential in today’s society. In camera-based assistive text reading framework to help blind person read text labels and product packaging from hand-held objects in their daily lives, simultaneously direct for the navigation of places with the help of signs which are basically in every public and private places. To isolate the object from cluttered backgrounds or other surrounding objects in the camera view, first an efficient and effective motion based method to define a region of interest (ROI) in the video by asking the user to shake the object. In this system, moving object region from background is extracted. In the extracted ROI, text localization and recognition are conducted to acquire text information. To automatically localize the text regions from the object ROI, a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels can be used. Text characters in the localized text regions are then binarized and recognized. Experimental results demonstrate algorithm achieves the state of the arts. User interface issues and assess robustness of the algorithm in extracting and reading text from different objects with complex backgrounds and extracted output component is use to inform the blind user of recognized text codes in the form of speech or audio. 1
  2. 2. 2. Literature Review No. Year of Publication Title of the paper Authors name Study 1 2014 Portable Camera- Based Assistive Text and Product Label Reading From Hand-Held Objects for Blind Persons Chucai Yi, Yingli Tian and Aries Arditi This is the main paper of this research, this paper totally focus recent developments in computer vision, digital cameras, and portable computers make it feasible to assist these individuals by developing camera-based products that combine computer vision technology with other existing commercial products such optical character recognition (OCR) systems. The corresponding feature maps estimate the global structural feature of text at every pixel. Adjacent character grouping is performed to calculate candidates of text patches prepared for text classification. An Adaboost learning model is employed to localize text in camera-based images. Off-the-shelf OCR is used to perform word recognition on the localized text regions and transform into audio output for blind users. 2
  3. 3. 2 2014 Information and Assisted Navigation System for Blind People Karen Duarte, Jos ´e Cec´ılio, Jorge S´a Silva, Pedro Furtado The system presented in this paper aims to highlight the user’s device integrating it with devices and technologies already used by users, as their own smartphone. So the location system is being developed based on Bluetooth technology, present in most parts of the mobile phones. After the environment is equipped with sufficient sensors, the system is able to locate the user and send him/her instructions that lead to the desired destination. Another important feature of the system is the accessible information system: the system allows the user to receive information about available stores, services or spaces. 3 2009 An algorithm enabling blind users to find and read barcodes Ender Tekin and James M. Coughlan In this paper, the ability of people who are blind or have significant visual impairments to read printed labels and product packages will enhance independent living and foster economic and social self- sufficiency. 4 2007 Text Sunil This paper proposes scheme for 3
  4. 4. Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model Kumar, Rajat Gupta, Nitin Khanna, Santanu Chaudhury and Shiv Dutt Joshi the extraction of textual areas of an image using globally matched wavelet filters. A clustering- based technique has been devised for estimating globally matched wavelet filters using a collection of groundtruth images and text extraction scheme for the segmentation of document images into text, background, and picture components 5 2003 Texture- Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm Kwang In Kim, Keechul Jung, and Jin Hyung Kim This paper show texture-based method for detecting texts in images. A support vector machine (SVM) is used to analyze the textural properties of texts. No external texture feature extraction module is used; rather, the intensities of the raw pixels that make up the textural pattern are fed directly to the SVM, which works well even in high- dimensional spaces. 3. Problem Statement 4
  5. 5. To overcome the problem in assistive reading systems for blind persons, in existing system very challenging for users to position the object of interest within the center of the camera’s view. As of now, there are still no acceptable solutions for exact location of bar code on the product. This problem approached in stages. The hand-held object should be appear in the camera view, for this use of camera with sufficiently wide angle to accommodate users with only approximate aim. On the same time system will allow to direct the direction navigation to blind person with the help of sign based system. This may often result in other text objects appearing in the camera’s view (for example, while shopping at a supermarket). To extract the hand-held object from the camera image, this system going to develop a motion-based method to obtain a region of interest (ROI) of the object with proper text recognition. And help the blind person with good audio quality as output of the system. 5
  6. 6. 4. System Architecture 4.1 Description The system architecture consists of three functional components: scene capture, data processing, and audio output. The scene capture component collects scenes containing objects of interest in the form of images or video. In our prototype, it corresponds to a camera attached to a pair of sunglasses. The data processing component is used for deploying our proposed algorithms, including 1) object- of- interest detection to selectively extract the image of the object held by the blind user from the cluttered background or other neutral objects in the camera view; and 2) text localization to obtain image regions containing text, and text recognition to transform image-based text information into readable codes. We use a laptop as the processing device in our current prototype system. The audio output component is to inform the blind user of recognized text codes. 6
  7. 7. 5. Possible Contribution The algorithm used in previous paper can handle complex background and multiple patterns, and extract text information from hand-held objects. In assistive reading systems for blind persons, it is very challenging for users to position the object of interest within the center of the camera’s view. As of now, there are still no acceptable solutions. In this system the previous drawback of algorithm can be minimized and divided the problem in stages. To make sure the hand-held object appears in the camera view, a camera with sufficiently wide angle to accommodate users with only approximate aim. This may often result in other text objects appearing in the camera’s view (for example, while shopping at a supermarket). To extract the hand-held object from the camera image, a motion-based method to obtain a region of interest (ROI) of the object is used. It is a challenging problem to automatically localize objects and text ROIs from captured images with complex backgrounds, because text in captured images is most likely surrounded by various background outlier “noise,” and text characters usually appear in multiple scales, fonts, and colors. For the text orientations, algorithm used in the previous paper assumes that text strings in scene images keep approximately horizontal alignment but that drawback of algorithm will overcome by algorithm which is best suitable. Many algorithms have been developed for localization of text regions in scene images. 7
  8. 8. 6. Time Schedule This is the tentative time for our plan of action. Work to be done /month Jul ’15 Aug ’ 15 Sep ’15 Oct ’15 Nov ’15 Dec ’15 Jan ’ 16 Feb ’16 Mar ’ 16 Apr ’ 16 May ’ 16 Jun ’ 16 Studying and analyzing different Data Stream algorithms and technique. Studying of literatures regarding Project Designing of algorithm for the dynamicity of privacy system Start implementing Project Phase I Phase II Phase III Phase IV Testing Thesis Preparation Phase I: Design of System Architecture. Phase II Implementation of Algorithms. Phase III: Verifications and Designs. Phase IV: Building Real Time System. 8
  9. 9. 7. Conclusion This paper has introduced to read printed text on hand-held objects for assisting blind persons. In order to solve the common aiming problem for blind users, a motion- based method to detect the object of interest is projected, while the blind user simply shakes the object for a couple of seconds. This method can effectively distinguish the object of interest from background or other objects in the camera view. An Adaboost learning model is employed to localize text in camera-based images .Off the shelf OCR is used to perform word recognition on the localized text regions and transform into audio output for blind users. 9
  10. 10. References [1] Chucai Yi, Yingli Tian and Aries Arditi ,”Portable Camera-Based Assistive Text and Product Label Reading From Hand-Held Objects for Blind Persons”, IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 19, NO. 3, JUNE 2014 [2] Karen Duarte, Jos´e Cec´ýlio, Jorge S´a Silva, Pedro Furtado “Information and Assisted Navigation System for Blind People”, Proceedings of the 8th International Conference on Sensing Technology, Sep. 2-4, 2014, Liverpool, UK [3] Sunil Kumar, Rajat Gupta, Nitin Khanna, Santanu Chaudhury and Shiv Dutt Joshi “Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 8, AUGUST 2007 [4] Kwang In Kim, Keechul Jung, and Jin Hyung Kim “Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 12, DECEMBER 2003 [5] Advance Data Reports from the National Health Interview Survey (2008).[Online]. Available: http://www.cdc.gov/nchs/nhis/nhis_ad.htm. [6] B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform,” in Proc. Comput. Vision Pattern Recognition, 2010, pp. 2963–2970. [7] C. Yi and Y. Tian, “Assistive text reading from complex background for blind persons,” in Proc. Int. Workshop Camera-Based Document Anal.Recognit., 2011, vol. LNCS-7139, pp. 15–28. [8] C. Yi and Y. Tian, “Text string detection from natural scenes by structure based partition and grouping,” IEEE Trans. Image Process., vol. 20, no. 9, pp. 2594–2605, Sep. 2011. [9] International Workshop on Camera-Based Document Analysis and Recognition (CBDAR 2005, 2007, 2009, 2011). [Online]. Available: http://www.m.cs.osakafuu.ac.jp/cbdar2011 10
  11. 11. Asst. Prof. Garima Singh Makhija Mr. Fazeel I. Z. Qureshi Head of CSE, WCEM Asst Professor, Project Coordinator Department of CSE Submitted By Under Guidance of Mr. Vivek R. Chamorshikar Asst. Prof. Saiyad Sharik Kaji M-Tech III Sem, Asst. Professor, Department of CSE 11

×