I know there are other posts about this, but I cannot seem to find one strictly for handwriting. I am going to have a form and all I need to read in is 8 squares in the left hand corner that will have 3 letters proceeded by 5 numbers.
The problem with most posts is that people either post about software for writing on the screen or software that doesn't recognize handwriting yet. I would prefer to have something in java, but something simple in another language would work.
What would really work is if people could scan their documents and just type the job number for the document name, but apparently they cant do that right...
Can you change the form? This problem will simplify a lot if you can change the form to be something that is easier for a machine to read. To recognize an arbitrary handwriting is hard as well as error prone.
What I have in mind is a form like this:
form example http://shareworldonline.com/w3/testprep/images/test%20form.jpg
However, if you have to have handwriting, check out the solutions in this thread.
if i got you correctly, you are doing offline hwr,
when i was doing offline hwr, i found most difficult separating characters in word, seems like you have them in squares, so all what you need to do is find your boxes (ie by using histogram)
and compare content of your box with each element in yours characters database (i used levenshtein distance for that)
I know it maybe not very helpful, but maybe push you on right track.
Related
I want to create an algorithm that searches job descriptions for given words (like Java, Angular, Docker, etc). My algorithm works, but it is rather naive. For example, it cannot detect the word Java if it is contained in another word (such as JavaEE). When I check for substrings, I have the problem that, for example, Java is recognized in the word JavaScript, which I want to avoid. I could of course make an explicit case distinction here, but I'm more looking for a general solution.
Are there any particular techniques or approaches that try to solve this problem?
Unfortunately, I don't have the amount of data necessary for data-driven approaches like machine learning.
Train a simple word2vec language model with your whole job description text data. Then use your own logic to find the keywords. When you find a match, if it's not an exact match use your similar words list.
For example you're searching for Java but find also javascript, use your word vectors to find if there is any similarity between them (in another words, if they ever been used in a similar context). Java and JavaEE probably already used in a same sentence before but java and javascript or Angular and Angularentwicklung been not.
It may seems a bit like over-engineering, but its not :).
I spent some time researching my problem, and I found that identifying certain words, even if they don't match 1:1, is not a trivial problem. You could solve the problem by listing synonyms for the words you are looking for, or you could build a rule-based named entity recognition service. But that is both error-prone and maintenance-intensive.
Probably the best way to solve my problem is to build a named entity recognition service using machine learning. I am currently watching a video series that looks very promising for the given problem. --> https://www.youtube.com/playlist?list=PL2VXyKi-KpYs1bSnT8bfMFyGS-wMcjesM
I will comment on this answer when I am done with my work to give feedback to those who are facing the same problem.
Greeting, wise ones!
I am trying to make a generator for pictures like this one. My idea is to make 2 patterns (vertical lines and horizontal lines). After that, I need to make vertical lines only appear "within" the letter but go a bit beyound if they don't intersect a horizontal line. Same for horizontal line, just for being "outside" the letter.
To perform this I need to know, which pixels are "within" letters of the text() object and which are not. This is the only thing, that I can't get my head around. Any ideas on how to implement this?
(If you have a simpler idea of how to make this generator, I'll happily read about them as well, I'm not too sure that mine is the best)
TL;DR: solution
So, initially, I was doing it in Java Processing and was thinking in terms of points and "if" conditions. I couldn't get any meaningful help and abandoned the problem, doing the thing by hand.
However, later I encountered a problem with detecting if a point is within a polygon in Unity and found a solution that includes the concept of raycasting. It's easily implemented in Unity but will require some extra work in something like Java Processing. In any case, this is an excellent answer to my question. I hope it helps anyone who encounters a similar problem.
I am working on Handwritten Form Recognition System, till now i have reached to this step where,i have been able to detect text using java with openCV but now i want to read the text from each of these bounding boxes Click to open image
I have being doing research to find out the process for the same using java with openCV but i was unable to find any.
Suggest me some links,Technologies,methods or process to perform this particular task with "JAVA".
This answer is more general than question specific. I will try to stick as much as possible with the problem statement.
Although there is a lot of on going research on recognition of hand written text, there is no full-proof method, which works with all possible problems.
The sample image you posted here is relatively noisy, with extremely high variance between the font of the same letter. This is exactly where it gets tricky.
I would personally suggest that once you have the bounding boxes around the text (which you already do), run contour extraction in all these bounding boxes in order to extract single letters. Once you have them, you need to figure out relevant feature/s that can represent the maximum variance (or at least 95% Confidence Interval) of the particular letter.
With this/ese feature/s, you need to train a supervised algorithm, letters as training data and their corresponding value (for eg. actual values) as labels. Once you have that, give it some data (the easiest and most difficult cases) to analyze the accuracy.
These links can help you for a start :
One of my first tools to check the accuracy with the set of features I use before I start coding: Weka
Go through basic tutorials on machine learning and how they work - Personal Favorite
You could try TensorFlow.
Simple Digit Recognition OCR in OpenCV-Python - Great for beginners.
Hope it helps!
I would like to know how practical it would be to create a program which takes handwritten characters in some form, analyzes them, and offers corrections to the user. The inspiration for this idea is to have elementary school students in other countries or University students in America learn how to write in languages such as Japanese or Chinese where there are a lot of characters and even the slightest mistake can make a big difference.
I am unsure how the program will analyze the character. My current idea is to get a single pixel width line to represent the stroke, compare how far each pixel is from the corresponding pixel in the example character loaded from a database, and output which area needs the most work. Endpoints will also be useful to know. I would also like to tell the user if their character could be interpreted as another character similar to the one they wanted to write.
I imagine I will need a library of some sort to complete this project in any sort of timely manner but I have been unable to locate one which meets the standards I will need for the program. I looked into OpenCV but it appears to be meant for vision than image processing. I would also appreciate the library/module to be in python or Java but I can learn a new language if absolutely necessary.
Thank you for any help in this project.
Character Recognition is usually implemented using Artificial Neural Networks (ANNs). It is not a straightforward task to implement seeing that there are usually lots of ways in which different people write the same character.
The good thing about neural networks is that they can be trained. So, to change from one language to another all you need to change are the weights between the neurons, and leave your network intact. Neural networks are also able to generalize to a certain extent, so they are usually able to cope with minor variances of the same letter.
Tesseract is an open source OCR which was developed in the mid 90's. You might want to read about it to gain some pointers.
You can follow company links from this Wikipedia article:
http://en.wikipedia.org/wiki/Intelligent_character_recognition
I would not recommend that you attempt to implement a solution yourself, especially if you want to complete the task in less than a year or two of full-time work. It would be unfortunate if an incomplete solution provided poor guidance for students.
A word of caution: some companies that offer commercial ICR libraries may not wish to support you and/or may not provide a quote. That's their right. However, if you do not feel comfortable working with a particular vendor, either ask for a different sales contact and/or try a different vendor first.
My current idea is to get a single pixel width line to represent the stroke, compare how far each pixel is from the corresponding pixel in the example character loaded from a database, and output which area needs the most work.
The initial step of getting a stroke representation only a single pixel wide is much more difficult than you might guess. Although there are simple algorithms (e.g. Stentiford and Zhang-Suen) to perform thinning, stroke crossings and rough edges present serious problems. This is a classic (and unsolved) problem. Thinning works much of the time, but when it fails, it can fail miserably.
You could work with an open source library, and although that will help you learn algorithms and their uses, to develop a good solution you will almost certainly need to dig into the algorithms themselves and understand how they work. That requires quite a bit of study.
Here are some books that are useful as introduct textbooks:
Digital Image Processing by Gonzalez and Woods
Character Recognition Systems by Cheriet, Kharma, Siu, and Suen
Reading in the Brain by Stanislas Dehaene
Gonzalez and Woods is a standard textbook in image processing. Without some background knowledge of image processing it will be difficult for you to make progress.
The book by Cheriet, et al., touches on the state of the art in optical character recognition (OCR) and also covers handwriting recognition. The sooner you read this book, the sooner you can learn about techniques that have already been attempted.
The Dehaene book is a readable presentation of the mental processes involved in human reading, and could inspire development of interesting new algorithms.
Have you seen http://www.skritter.com? They do this in combination with spaced recognition scheduling.
I guess you want to classify features such as curves in your strokes (http://en.wikipedia.org/wiki/CJK_strokes), then as a next layer identify componenents, then estimate the most likely character. All the while statistically weighting the most likely character. Where there are two likely matches you will want to show them as likely to be confused. You will also need to create a database of probably 3000 to 5000 characters, or up to 10000 for the ambitious.
See also http://www.tegaki.org/ for an open source program to do this.
I was wondering if anyone had experience retrieving data with the 3270 protocol. My understanding so far is:
Connection
I need to connect to an SNA server using telnet, issue a command and then some data will be returned. I'm not sure how this connection is made since I've read that a standard telnet connection won't work. I've also read that IBM have a library to help but not got as far as finding out any more about it.
Parsing
I had assumed that the data being returned would be a string of 1920 characters since the 3278 screen was 80x24 chars. I would simply need to parse these chars into the appropriate fields. The more I read about the 3270 protcol the less this seems to be the case - I read in the documentation provided with a trial of the Jagacy 3270 Java library that attributes were marked in the protocol with the char 'A' before the attribute and my understanding is that there are more chars denoting other factors such as whether fields are editable.
I'm reasonably sure my thinking has been too simplistic. Take an example like a screen containing a list of items - pressing a special key on one of the 24 visible rows drills down into more detailed information regarding that row.
Also it's been suggested to me that print commands can be issued. This has some positive implications - if the format of the string returned is not 1920 since it contains these characters such as 'A' denoting how users interact with the terminal, printing would eradicate these. Also it would stop having to page through lots of data. The flip side is I wouldn't know how to retrieve the data from the print command back to Java.
So..
I currently don't have access to the SNA server but have some screen shots of what the terminal will look like once I get a connection and was therefore going to start work on parsing. With so many assumptions and not a lot of idea on what the data will look like I feel really stumped. Does anyone have any knowledge of these systems that might help me back on track?
You've picked a ripper of a problem there. 3270 is a very complex protocol indeed. I wouldn't bother about trying to implement it, it's a fool's errand, and I'm speaking from painful personal experience. Try to find a TN3270 (Telnet 3270) client API.
This might not specifically answer your question, but...
If you are using Rational Developer for z/OS, your java code should be able to use the integrated HATS product to deal with the 3270 stream. It might not fit your project, but I thought I would mention it if all you are trying to do is some simple screen scraping, it makes things very easy.