There are many research on Automated Speech Recognition that convert speech to text. These tools are using deep learning to do that.
I have found that the way it works is based on the english language. If audio of word "Phonics" they will be either Foniks but the closest english word for that is Phonics.
Google APIs can provide us with ASR that gives us the end result. Is there any tool or open source that can give us the phonics sounds? Something like this "ˈfəʊnɪks" instead of "Phonics"
Thanks.
There are several open source tools for ASR. Kaldi, CMU Sphinx and HTK are the most popular and well documented. Kaldi will be probably the best if you want to use DNNs for ASR.
However, the form of recognition result provided depends on your vocabulary. If you wish to have a word ˈfəʊnɪks instead of Phonics, you have to define it in the vocabulary. For instance:
!SIL sil
<UNK> spn
eight ey t
five f ay v
...
f_ey_ow_n_i_k_s f ey ow n i k s
....
Using Unicode symbols for word representation is not possible (as far as I remember), so I replaced them with X-SAMPA notation.
Follow this tutorial for in-depth explanation.
We have a history of conversations between humans (any language, any vocabulary), so with a lof of spelling errors:
"hellobb do u hav skip?" => "hello baby, do you have skype?"
Before running a deep learning task against this data set (find synonyms etc..), I would like to fix these errors.
Is it a good idea? I've never worked with such bad quality data. Wondering if there is a "magic solution" to achieve this.
Else I plan to use:
word embeddings (word2vec) to check if good and bad words are similar
distance function between words
if wordA is less famous wordB then fix(wordA) = wordB
There is no magic solution at this moment to guaranty to fix all misspelling errors on your text but here are some possible options you can consider:
Dictionary-based approach. I found Hunspell very handy in this case. It uses language modeling and Levenshtein distance to suggest the correct spelling. It is available on many natural & programming languages. Although it is a dictionary-based approach, it is superior to many sophisticated approaches. It is used in vast majority word-processing applications.
Statistical and traditional approach. Another possible solution is to develop your own statistical models such as language modeling. Training language modeling on a large corpus, at character level & word level, can found many misspelling on the text. Many speech recognition and search engines use language modeling at their heart to fix the misspelling.
Deep learning approach. If you look at NLPProgress.com, most of the state-of-the-art research used seq2seq models to attack grammatical error problem. The main intuition behind these models is to train a neural network on pairs of sentences which network learns how to fix grammatical error. These approaches require quite a lot of pairs sentence to gives a reliable result. If the available corpora are not fit to your needs, you can generate your own misspelling e.g. by replacing some tokens in your text.
I’m trying to reverse engineer an old medical imaging format called Stentor for interoperability. It was designed by a company of the same name who was subsequently bought by Phillips. But Phillips has forgotten how to read Stentor files. I have a windows program which exports JPEG from Stentor files but it’s closed source. I’d like to automate this process in order to tackle hundreds of files in this format.
The program is late-1990s Win32 or MFC executeable. It runs next to an ActiveX (.ocx) file which I’ve been able to interop with, but that file doesn’t contain the export method. I'm looking for suggestions on how to dissemble the binary in order to unearth the algorithm used to convert Stentor to JPEG. I looked through the Stentor files in hex editor and didn’t find any evidence of JPEG (although hints on finding that would be appreciated too), so I think that the program has a couple of tricks up its sleeve.
Thanks in advance.
Kyle
Few programmers implement complex routines such as image recoding themselves. Instead they tend to license libraries that do that. A very smart way to start would be searching for text strings and see if you can discover the libraries they use. This will subsequently give you a lot of insight into how the data is encoded.
Another good strategy would be to build a program that simply runs the GUI of your export program by sending mouse and keyboard events directly to it. Let this run a few days to complete your export. Reverse engineering the file format is going to be slow and expensive so for a 1 time gig it's probably not worthwhile.
I need an open OCR library which is able to scan complex printed math formulas (for example some formulas which were generated via LaTeX). I want to get some LaTeX-like output (or just some AST-like data).
Is there something like this already? Or are current OCR technics just able to parse line-oriented text?
(Note that I also posted this question on Metaoptimize because some people there might have additional knowledge.)
The problem was also described by OpenAI as im2latex.
SESHAT is a open source system written in C++ for recognizing handwritten mathematical expressions. SESHAT was developed as part of a PhD thesis at the PRHLT research center at Universitat Politècnica de València.
An online demo:http://cat.prhlt.upv.es/mer/
The source: https://github.com/falvaro/seshat
Seshat is an open-source system for recognizing handwritten mathematical expressions. Given a sample represented as a sequence of strokes, the parser is able to convert it to LaTeX or other formats like InkML or MathML.
According to the answers on Metaoptimize and the discussion on the Tesseract mailinglist, there doesn't seem to be an open/free solution yet which can do that.
The only solution which seems to be able to do it (but I cannot verify as it is Windows-only and non-free) is, like a few other people have mentioned, the InftyProject.
InftyReader is the only one I'm aware of. It is NOT free software (it seems the money goes to a non-profit org, IIRC).
http://www.sciaccess.net/en/InftyReader/
I don't know why PDF can't have metadata in LaTeX? As in: put the LaTeX equation in it! Is this so hard? (I dunno anything about PDF syntax, but I imagine it can be done).
LaTeX syntax is THE ONE TRIED AND TRUE STANDARD for mathematics notation. It seems amazingly stupid that folks that produced MathML and other stuff don't take this in consideration. InftyReader generates MathML or LaTeX syntax.
If I want HTML (pure) I then use TTH to read the LaTeX syntax. Just works.
ABBYY FineReader (a great OCR program) claims you can train the software for Math, but this is immensely braindead (who has the time?)
And Unicode has lots of math symbols. That today's OCR readers can't grok them shows the sorry state of software and the brain deficit in this activity.
As to "one symbol at a time", TeX obviously has rules as to where it will place symbols. They can't write software that know those rules?! TeX is even public domain! They can just "use it" in their comercial products.
Check out "Web Equation." It can convert handwritten equations to LaTeX, MathML, or SymbolTree. I'm not sure if the engine is open source.
Considering that current technologies read one symbol at a time (see http://detexify.kirelabs.org/classify.html), I doubt there is an OCR for full mathematical equations.
Infty works fairly well. My former company integrated it into an application that reads equations out loud for blind people and is getting good feedback from users.
http://www.inftyproject.org/en/download.html
Since the output from math OCR for complex formulas will likely have bugs -- even humans have trouble with it -- you will have to proofread th results, at least if they matter. The (human) proofreader will then have to correct the results, meaning you need to have a math formula editor. Given the effort needed by humans, the probably limited corpus of complex formulas, you might find it easier to assign the task to humans.
As a research problem, reading math via OCR is fun -- you need a formalism for 2-D grammars plus a symbol recognizer.
In addition to references already mentioned here, why not google for this? There is work that was done at Caltech, Rochester, U. Waterloo, and UC Berkeley. How much of it is ready to use out of the box? Dunno.
As of August 2019, there are a few options, depending on what you need:
For converting printed math equations/formulas to LaTex, Mathpix is absolutely the best choice. It's free.
For converting handwritten math to LaTex or printed math, MyScript is the best option, although its app costs a few dollars.
You know, there's an application in Win7 just for that: Math Input Panel. It even handles handwritten input (it's actually made for this). Give it a shot if you have Win7, it's free!
there is this great short video: http://www.youtube.com/watch?v=LAJm3J36tLQ
explaining how you can train your Fine Reader to recognize math formulas. If you use Fine Reader already, better to stick with one tool. Of course it is not free ware :(
What tools are available to aid in decoding unknown binary data formats?
I know Hex Workshop and 010 Editor both support structures. These are okay to a limited extent for a known fixed format but get difficult to use with anything more complicated, especially for unknown formats. I guess I'm looking at a module for a scripting language or a scriptable GUI tool.
For example, I'd like to be able to find a structure within a block of data from limited known information, perhaps a magic number. Once I've found a structure, then follow known length and offset words to find other structures. Then repeat this recursively and iteratively where it makes sense.
In my dreams, perhaps even automatically identify possible offsets and lengths based on what I've already told the system!
Here are some tips that come to mind:
From my experience, interactive scripting languages (I use Python) can be a great help. You can write a simple framework to deal with binary streams and some simple algorithms. Then you can write scripts that will take your binary and check various things. For example:
Do some statistical analysis on various parts. Random data, for example, will tell you that this part is probably compressed/encrypted. Zeros may mean padding between parts. Scattered zeros may mean integer values or Unicode strings and so on. Try to spot various offsets. Try to convert parts of the binary into 2 or 4 byte integers or into floats, print them and see if they make sence. Write some functions that will search for repeating or very similar parts in the data, this way you can easily spot headers.
Try to find as many strings as possible, try different encodings (c strings, pascal strings, utf8/16, etc.). There are some good tools for that (I think that Hex Workshop has such a tool). Strings can tell you a lot.
Good luck!
For Mac OS X, there's a great tool that's even better than my iBored: Synalyze It!
(http://www.synalysis.net/)
Compared to iBored, it is better suited for non-blocked files, while also giving full control over structures, including scriptability (with Lua). And it visualizes structures better, too.
Tupni; to my knowledge not directly available out of Microsoft Research, but there is a paper about this tool which can be of interest to someone wanting to write a similar program (perhaps open source):
Tupni: Automatic Reverse Engineering of Input Formats (# ACM digital library)
Abstract
Recent work has established the importance of automatic reverse
engineering of protocol or file format specifications. However, the
formats reverse engineered by previous tools have missed important
information that is critical for security applications. In this
paper, we present Tupni, a tool that can reverse engineer an input
format with a rich set of information, including record sequences,
record types, and input constraints. Tupni can generalize the format
specification over multiple inputs. We have implemented a
prototype of Tupni and evaluated it on 10 different formats: five
file formats (WMF, BMP, JPG, PNG and TIF) and five network
protocols (DNS, RPC, TFTP, HTTP and FTP). Tupni identified all
record sequences in the test inputs. We also show that, by aggregating
over multiple WMF files, Tupni can derive a more complete
format specification for WMF. Furthermore, we demonstrate the
utility of Tupni by using the rich information it provides for zeroday
vulnerability signature generation, which was not possible with
previous reverse engineering tools.
My own tool "iBored", which I released just recently, can do parts of this. I wrote the tool to visualize and debug file system formats (UDF, HFS, ISO9660, FAT etc.), and implemented search, copy and later even structure and templates support. The structure support is pretty straight-forward, and the templates are a way to identify structures dynamically.
The entire thing is programmable in a Visual BASIC dialect, allowing you to test values, read specific blocks, and all.
The tool is free, works on all platforms (Win, Mac, Linux), but as it's personal tool which I just released to the public to share it, it's not much documented.
However, if you want to give it a try, and like to give feedback, I might add more useful features.
I'd even open source it, but as it's written in REALbasic, I doubt many people will join such a project.
Link: iBored home page
I still occasionally use an old hex editor called A.X.E., Advanced Hex Editor. It seems to have largely disappeared from the Internet now, though Google should still be able to find it for you. The last version I know of was version 3.4, but I've really only used the free-for-personal-use version 2.1.
Its most interesting feature, and the one I've had the most use for deciphering various game and graphics formats, is its graphical view mode. That basically just shows you the file with each byte turned into a color-coded pixel. And as simple as that sounds, it has made my reverse-engineering attempts a lot easier at times.
I suppose doing it by eye is quite the opposite of doing automatic analysis, though, and the graphical mode won't be much use for finding and following offsets...
The later version has some features that sound like they could fit your needs (scripts, regularity finder, grammar generator), but I have no idea how good they are.
There is Hachoir which is a Python library for parsing any binary format into fields, and then browse the fields. It has lots of parsers for common formats, but you can also write own parsers for your files (eg. when working with code that reads or writes binary files, I usually write a Hachoir parser first to have a debugging aid). Looks like the project is pretty much inactive by now, though.
Kaitai is an open-source language for describing binary structures in data streams. It comes with a translator that can output parsing code for many programming languages, for inclusion in your own program code.
My project icebuddha.com supports this using python to describe the format in the browser.
A cut'n'paste of my answer to a similar question:
One tool is WinOLS, which is designed for interpreting and editing vehicle engine managment computer binary images (mostly the numeric data in their lookup tables). It has support for various endian formats (though not PDP, I think) and viewing data at various widths and offsets, defining array areas (maps) and visualising them in 2D or 3D with all kinds of scaling and offset options. It also has a heuristic/statistical automatic map finder, which might work for you.
It's a commercial tool, but the free demo will let you do everything but save changes to the binary and use engine management features you don't need.