Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure

Shear-wave and spatial attributes in time-lapse 3-D/3-C seismic and potential-field datasets

In this study, I utilize multicomponent time-lapse seismic datasets for investigating subtle seismic properties of Weyburn reservoir undergoing enhanced oil recovery and geologic sequestration of CO2. The primary focus is on extracting shear-wave information from surface three-dimensional and three-component (3-D/3-C) reflection datasets. Four groups of interrelated objectives are addressed: 1) calibrated and true-amplitude processing of multicomponent time-lapse seismic data, 2) extraction of amplitude variations with angle (AVA) and offset (AVO) attributes for separating pressure and fluid-saturation effects within the reservoir, 3) development of receiver-function methods for investigating the shallow subsurface, and 4) 2-D spatial pattern analysis of attribute maps, intended for automated interpretation of the results and a new type of AVO analysis. To achieve the first of these objectives, I reprocess the field surface 3-C/3-D reflection datasets by using pre-stack waveform calibration followed by complete reflection processing using commercial ProMAX software. For the second, principal objective of this study, several AVA attributes of the reservoir are examined, including those related to P- and P/S- converted waves and P- and S-wave impedances. The amplitudes and AVA attributes derived from seismic data indicate temporal variations potentially caused by pore-pressure and CO2-saturation variations within the reservoir. By comparing with AVA forward models, the seismic data suggest correlations between the increasing pore pressure and decreasing AVA intercepts and increasing AVA gradients. Increasing CO2 saturations appear to correlate with simultaneously decreasing AVA intercepts and gradients. CO2-saturated zones are thus interpreted as Class III AVA anomalies. In order to take further advantage from 3-C recordings and investigate advanced methods for S-wave seismic data analysis, receiver functions are used to study the shallow near-surface structure. This is apparently the first application of this method to reflection seismic datasets on land and in a time-lapse 3-D dataset. I show that it is feasible and useful to measure the near-surface S-wave velocity structure by using multi-component seismic data. From Weyburn reflection data, the average mapped receiver-function time lags are about 35 ms, which corresponds to near-surface S-wave velocities of about 550 m/s. Time-lapse variations of the near-surface structure are measured, and S-wave statics models are derived. Such models can be useful for converted-wave seismic imaging. The last objective of this Dissertation is to develop tools for interpretation of gridded 2-D spatial images, such as mapping AVO attribute quantitatively and automatically. For this purpose, a new pattern-recognition approach called skeletonization is developed and applied to several regional aeromagnetic and gravity images from southern Saskatchewan and Manitoba. The approach is combined with 2-D empirical mode decomposition allowing pattern analysis at variable spatial scales. The results show that skeletonization helps identifying complex geologic structures and measuring their quantitative attributes that are not available from conventional interpretation. Applications of this approach to interpretation of AVO attributes are discussed

Vision Based Extraction of Nutrition Information from Skewed Nutrition Labels

An important component of a healthy diet is the comprehension and retention of nutritional information and understanding of how different food items and nutritional constituents affect our bodies. In the U.S. and many other countries, nutritional information is primarily conveyed to consumers through nutrition labels (NLs) which can be found in all packaged food products. However, sometimes it becomes really challenging to utilize all this information available in these NLs even for consumers who are health conscious as they might not be familiar with nutritional terms or find it difficult to integrate nutritional data collection into their daily activities due to lack of time, motivation, or training. So it is essential to automate this data collection and interpretation process by integrating Computer Vision based algorithms to extract nutritional information from NLs because it improves the user’s ability to engage in continuous nutritional data collection and analysis. To make nutritional data collection more manageable and enjoyable for the users, we present a Proactive NUTrition Management System (PNUTS). PNUTS seeks to shift current research and clinical practices in nutrition management toward persuasion, automated nutritional information processing, and context-sensitive nutrition decision support. PNUTS consists of two modules, firstly a barcode scanning module which runs on smart phones and is capable of vision-based localization of One Dimensional (1D) Universal Product Code (UPC) and International Article Number (EAN) barcodes with relaxed pitch, roll, and yaw camera alignment constraints. The algorithm localizes barcodes in images by computing Dominant Orientations of Gradients (DOGs) of image segments and grouping smaller segments with similar DOGs into larger connected components. Connected components that pass given morphological criteria are marked as potential barcodes. The algorithm is implemented in a distributed, cloud-based system. The system’s front end is a smartphone application that runs on Android smartphones with Android 4.2 or higher. The system’s back end is deployed on a five node Linux cluster where images are processed. The algorithm was evaluated on a corpus of 7,545 images extracted from 506 videos of bags, bottles, boxes, and cans in a supermarket. The DOG algorithm was coupled to our in-place scanner for 1D UPC and EAN barcodes. The scanner receives from the DOG algorithm the rectangular planar dimensions of a connected component and the component’s dominant gradient orientation angle referred to as the skew angle. The scanner draws several scan lines at that skew angle within the component to recognize the barcode in place without any rotations. The scanner coupled to the localizer was tested on the same corpus of 7,545 images. Laboratory experiments indicate that the system can localize and scan barcodes of any orientation in the yaw plane, of up to 73.28 degrees in the pitch plane, and of up to 55.5 degrees in the roll plane. The videos have been made public for all interested research communities to replicate our findings or to use them in their own research. The front end Android application is available for free download at Google Play under the title of NutriGlass. This module is also coupled to a comprehensive NL database from which nutritional information can be retrieved on demand. Currently our NL database consists of more than 230,000 products. The second module of PNUTS is an algorithm whose objective is to determine the text skew angle of an NL image without constraining the angle’s magnitude. The horizontal, vertical, and diagonal matrices of the (Two Dimensional) 2D Haar Wavelet Transform are used to identify 2D points with significant intensity changes. The set of points is bounded with a minimum area rectangle whose rotation angle is the text’s skew. The algorithm’s performance is compared with the performance of five text skew detection algorithms on 1001 U.S. nutrition label images and 2200 single- and multi-column document images in multiple languages. To ensure the reproducibility of the reported results, the source code of the algorithm and the image data have been made publicly available. If the skew angle is estimated correctly, optical character recognition (OCR) techniques can be used to extract nutrition information

単眼画像入力型ビューベース3Dモデル検索に関する研究

Tohoku University北村喜文課