Far-Red Photography for Measuring Plant Growth: A Novel Approach

A critical part of agricultural studies is determining plant stress and growth rate. Modern computer vision provides a series of tools that can be applied to derive this data. In this paper, we will show our findings, analyze their accuracy, and define a system capable of deriving this data with near-human accuracy in a fraction of the time. Denoising techniques applicable to this system will be discussed, as will our discoveries and findings. Finally, suggestions for further research opportunities will be provided

Interface Development for Digitization of Documents Using OCR

The purpose of this thesis is to develop a semi-automated interface that uses Optical Character Recognition (OCR) routines to identify text-based information from a large volume of digitized drawings associated with the oil and gas industry. The identified information is presented in an appropriate interface for any necessary manual modifica- tion, with the target of improving the efficiency of maintaining large amounts of older documents. The thesis outlines the design of the interface and the implementation of Tesseract OCR engine, in combination with tailor-made functions and classes that lever- age OpenCV to enhance the recognition process.The purpose of this thesis is to develop a semi-automated interface that uses Optical Character Recognition (OCR) routines to identify text-based information from a large volume of digitized drawings associated with the oil and gas industry. The identified information is presented in an appropriate interface for any necessary manual modifica- tion, with the target of improving the efficiency of maintaining large amounts of older documents. The thesis outlines the design of the interface and the implementation of Tesseract OCR engine, in combination with tailor-made functions and classes that lever- age OpenCV to enhance the recognition process

Interface Development for Digitization of Documents Using OCR

The purpose of this thesis is to develop a semi-automated interface that uses Optical Character Recognition (OCR) routines to identify text-based information from a large volume of digitized drawings associated with the oil and gas industry. The identified information is presented in an appropriate interface for any necessary manual modification, with the target of improving the efficiency of maintaining large amounts of older documents. The thesis outlines the design of the interface and the implementation of Tesseract OCR engine, in combination with tailor-made functions and classes that leverage OpenCV to enhance the recognition processThe purpose of this thesis is to develop a semi-automated interface that uses Optical Character Recognition (OCR) routines to identify text-based information from a large volume of digitized drawings associated with the oil and gas industry. The identified information is presented in an appropriate interface for any necessary manual modification, with the target of improving the efficiency of maintaining large amounts of older documents. The thesis outlines the design of the interface and the implementation of Tesseract OCR engine, in combination with tailor-made functions and classes that leverage OpenCV to enhance the recognition proces

Pipeline for Calculating Calories for Print Recipes with Minimal User Intervention

The thesis will provide a pipeline to estimate calorie counts from print recipes. The pipeline takes scanned recipes from cookbooks and uses Optical Character Recognition (OCR) to convert the scanned images of recipes to text. Several OCR tools were tested for their accuracy on fractions using a sample of the data, and the most accurate tool is used on the data. Next, a specially trained named entity recognition model is used to identify ingredients, quantities and units. These ingredients are used to search a database of values from the FDA to compute a calorie count for the recipe. The thesis tests the effectiveness of search by examining performance over 100 of the most common ingredients in the corpus of recipes. Finally, the thesis tests the performance of the model on a set of recipes, and found to estimate the calorie count at least as accurately as other automated approaches, such as those based on image recognition

FPGA in image processing supported by IOPT-Flow

Image processing is widely used in the most diverse industries. One of the tools widely used to perform image processing is the OpenCV library. Although the implementation of image processing algorithms can be made in software, it is also possible to implement image processing algorithms in hardware. In some cases, the execution time can be smaller than the execution time achieved in software. This work main goal is to evaluate the use of VHDL, DS-Pnets, and IOPT-Flow to develop image processing systems in hardware, in FPGA-based platforms. To enable it, a validation platform was developed. A set of image processing algorithms were specified, during this work, in VHDL and/or in DS-Pnets. These were validated using the IOPT-Flow validation tool and/or the Xilinx ISE Simulator. The automatic VHDL code generator from IOPT-Flow framework was used to translate DS-Pnet models into the implementation code. The FPGA-based implementations were compared with software implementations, supported by the OpenCV library. The created DS-Pnet models were added into a folder of the IOPT-Flow editor, to create an image processing library. It was possible to conclude that the DS-Pnets and their associated tools, IOPT-Flow tools, support the development of image processing systems. These tools, which simplify the development of image processing systems, are available online at http://gres.uninova.pt/iopt-flow/

Text segmentation and recognition in unconstrained imagery

Abstract: In this paper, we present a novel method for recognizing and segmenting symbols and text in complex image sequences. The algorithm is designed to take advantage of the massive computing capability of parallel processing architectures..

Adaptive Binarization of Unconstrained Hand-Held Camera-Captured Document Images

Abstract: This paper presents a new adaptive binarization technique for degraded hand-held camera-captured document images. The state-of-the-art locally adaptive binarization methods are sensitive to the values of free parameter. This problem is more critical when binarizing degraded camera-captured document images because of distortions like non-uniform illumination, bad shading, blurring, smearing and low resolution. We demonstrate in this paper that local binarization methods are not only sensitive to the selection of free parameters values (either found manually or automatically), but also sensitive to the constant free parameters values for all pixels of a document image. Some range of values of free parameters are better for foreground regions and some other range of values are better for background regions. For overcoming this problem, we present an adaptation of a state-of-the-art local binarization method such that two different set of free parameters values are used for foreground and background regions respectively. We present the use of ridges detection for rough estimation of foreground regions in a document image. This information is then used to calculate appropriate threshold using different set of free parameters values for the foreground and background regions respectively. The evaluation of the method using an OCR-based measure and a pixel-based measure show that our method achieves better performance as compared to state-of-the-art global and local binarization methods