Extract data from images of graphs (reverse engineer the data)

Hello. I am interested in your job. I am a expert in reverse engineering, c++, Java. if u'd like, i'd be happy to work with u. Thanks.

Hello! I have used MATLAB for some time for both image and time series processing as part of my DSP profession. I am sure we can work together quite nicely. Contact me if we need to discuss more. All the best! Ognjen

I have 8 years experience in image processing and worked on several projects. I can do this project in Matlab for fast result and save the data as text or excel format. it has two parts one is detecting the points and then recognizing the number. So it is kind of OCR system. Please check my profile and let me know if I can help you in this area. We can have chat on Skype sml2014freelancer for further discussion. I can do it in C++ with opencv but this will cost more as need more time for programming. Thanks Seyed

Hello, We will use contrast based algorithm, first we will convert image to gray scale and then use threshold level to extract information as we typically do for maps..............................Prefer offer at $55/hr rtae with $550 as INITIAL MILESTONE

Hello, I'll use C#, probably with Tesseract OCR to read the x and y ranges. Are the images always the same size (like the sample images) ? The min and max range are always in the same position, the program can read that (x will have to be rotated 45º) and calculate the days (or hours) per pixel, or $ in the y axis. Since the chart line is always the same unique color, it will be easy to get line position for each x.

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Dear sir, I have experience on data thief from images. It is very easy. I use a software very efficiency. If you desire a demostration, send me an email to jlmeseguer508 by hotmail Best regards.

I have worked on decoding of png images before. After reviewing the sample images, I find that they are all of same size, use same character definitions at 'similar' places & the images can be divided into zones. The approach I had used earlier &which I propose now searches for a 100% match unlike conventional OCR/machine learning techniques which have accuracy number associated with it. I shall 'train' my solution by providing all possible inputs. For example, for the Y axis if I were to provide the definitions of the "$", "1", "2",...."0" from the sample images provided, then my solution shall scan the image and any 100% match shall be detected. This way we shall get the data recognised with location which can then be assimilated. Same holds good for the X axis labels, though we shall have to scan several images to get a "complete definition set". The X axis labels pose a problem due to orientation & also the year is mentioned separately on a different line, relative location also changes. Key point: Approach needs validation for 100% match criteria. Willing to invest some time to test a case. Shall use freely available lodePNG source code for decoding files, rest fully custom code. Proposal: Budget and time to be re-assessed based on following clarifications: What exactly is output format for each image: csv file? And its content: just Y & X axis labels, the red color scan point data? Please share sample file. Also, Please let me know how many total number of images