What is the best way to go about reading characters in a grid-like format, like a Sudoku puzzle? I tried using this tutorial to get started with Tesseract OCR, but it was pretty inconsistent even with similar fonts. I want to be able to read a pattern of characters and store it in a multidimensional array. How can I train Tesseract to do this like I would train it to interpret different fonts?
I would go with OpenCV (http://opencv.org/) to interpret the Sudoku grid and then I would recognise the numbers using Tesseract. You can check these tutorials: https://www.raywenderlich.com/93276/implementing-tesseract-ocr-ios, https://github.com/BloodAxe/OpenCV-Tutorial and https://github.com/aptogo/OpenCVForiPhone. I hope it helps you.
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I am currently developing a commercial software. I need to add the feature of chinese character and word detection but it seems the functions of Scene Text Detection can only detect english characters and words. I searched on google and nothing related showed up.
I will feed a scanned A4 paper image to the application for it to find some chinese words based on some pre-set conditions.For example, the image contains the word "你好"(it means "Hello" in chinese) twice but only extract it once and save it as a string when it meets the pre-set condition of it is next to the title of 姓名(Name).
Here is a small illustration of the example:
Greeting: 你好
姓名（Name）: 你好 <--- this word detection only
Can someone please, who has decent experience with opencv or emgucv help me out?
If a custom dataset is needed in order to achieve my goal, can someone guide me on how to perform dataset training for word detection in opencv or emgucv.
(OpenCV or EmguCV is not your solution) You need Deep Neural NetWork(DNN) such as TensorFlow
I am new in Maxima. I have a set of data, (x,y,error) and I want to fit a linear line on it. I found some examples in example by maxima "Chapter 5: 2D Plots and Graphics using qdraw " but honestly I don't know how to download and use "qdraw" package.
anyone can help?
I see that qdraw.mac is linked from the page you mentioned. Maybe you can search for qdraw.mac on that page.
Maxima has some capability to work with linear regression models, but other packages which are specifically devoted to statistics might be more suitable. Have you tried R? (http://www.r-project.org)
I'm a complete beginner in character recognition as well as machine learning in general.
I want to write a program which is able to process the following input:
A Chinese character (in either pixels of vector format), for example:
The decomposition of the previous character, ie for the example above:
and and the information that they are aligned horizontally.
The decomposition of a Chinese character is always 3 things: 2 other characters and the pattern describing how the 2 character form the initial character (it is called the compoisition kind). In the example above the composition kind is "aligned horizontally".
Given such an input, I want my program to tell which pixels or which contours in the initial character belongs to which subcharacter in its decomposition.
Where to start?
Well, I can't say that I provide a full answer but think about:
1) Reading the papers on how Google Translate app works. You know, when you point your iPhone's camera at text and it instantly translates the text (even preserving the fonts!). It supports the chineese language so it would be interesting for you to see if they solved similar task and how they did it
2) Another big question to answer - how to prepare your input data. You will need to provide at least some input data - i.e. decomposition of at least some characters. Try to do this manually for couple of characters and try to formalize what exactly you are doing - this will help you to better formulate what exactly you want your algorithm to do.
3) Try to use some deep neural net with your data from #2. Use something with convolution layers. Pre-train it with RBM (restricted boltzmann machine). After that - just take a really close look into the resulting neural network. Don't expect to get any good results, but looking into the ANN layers will help you to understand what the net have learned from data and might provide some insight into where to move next
I am working on comparing 2 word images like these:
I am trying to use OpenCV's inbuilt feature detectors for this purpose.Using SIFT and BruteForceMatcher isn't proving to be too effective as it shows a lot of matches for non-matching words also.What would be a good feature descriptor to use in this case.
Also can someone suggest a good way to quantify the probability that these 2 images are matching.
Edit:OCR can't be used as texts can be handwritten
I think that image (contour) moments should help you with matching selected symbol to latter from your alphabet. So after this you can match two words as sequences of letters.
I'm confused about the way I should make the "features extraction " method
I want to use SVMs to apply "Object recognition" in images ,
There's a sample in Emgu's examples that holds an XML file contains the features of a cat !
and I've been trying since a week to know how they did it and what methods they used
and I came across this page
http://experienceopencv.blogspot.com/2011/02/learning-deformable-models-with-latent.html
that displays the steps ! It's so complicated plus couldn't do it myself
I'm so lost !! can anyone tell me an appropriate method of "features extraction "Compatible with SVMs learning ?
Accord has SVM example but it's on hand writing and doesn't deal with color images =(
any helping links ?
thanks
all feature extraction methods are compatible with svm... u just need to choose one... select one and get the features and then input these features into svm.... explanation of what is feature extraction is here http://en.wikipedia.org/wiki/Feature_extraction
You need to concentrate on the gabor filter, which is an advanced extractor for face recognition and object recognition.