Would it be a problem for you if I rewrite the opencl engine completely, and you people provide me help to link the tesseract kernel -> opencl engine parts? in attachment, I already have a list of features I'd like to port to openCL. As this uses the GPU in a heavy way, I will implement multi-card support on the host. Is it a problem for you guys to think of tesseract 5.0 as a milestone?
2018-04-27 15:53 GMT+00:00 Janpieter Sollie <[email protected]>: > if I'm right, a neural net is about the engine parts, not the image > characterisation rendering method, am I right? because I see many > presentations, and most of them talk about the history of tesseract, but > that's not what I need > > 2018-04-27 14:27 GMT+00:00 ShreeDevi Kumar <[email protected]>: > >> Please see >> >> https://github.com/tesseract-ocr/tesseract/wiki/4.0-with-LSTM >> >> For info about neural nets used by tesseract >> >> On Fri 27 Apr, 2018, 7:48 PM Janpieter Sollie, <[email protected]> >> wrote: >> >>> I had a quick thought about what you could offload to opencl. I will >>> need some help from you people (I am a C programmer, not C++, at least not >>> experienced) to do the host code, but this algorithm is perfectly >>> optimizeable in openCL. >>> the way I'd do it: >>> >>> prerequirements: >>> - you can define 65k offsets (x,y) in whose you want the openCL engine >>> to look for dots (x,y), the optimal position and closest neighbour can be >>> reported in the first part. >>> - you can make a RAW image of both the image and the characters. size of >>> the letters doesn't matter, but they must be trimmed properly >>> >>> 1. you give me a matrix of 256*256 offsets(short, short) to analyze, >>> with a max of 64 dots (char, char) (I assume these are neurons) to analyze >>> in each offset. >>> so, this gives you a start memory usage of 2⁸ * 2⁸ *4 + 64*2 = 256k + >>> 128 bytes >>> each dot MUST contain a black pixel. >>> then we add the image, this is a charimage of max (to be discussed with >>> you guys), I assume a 4096*4096 pixel image would be fine, especially when >>> a character can contain a 4x4 matrix defining a 0/1 (black/white) value. >>> 2. Then I follow these steps in the openCL engine: >>> - we analyze the neurons >>> - draw a cirle around them of x black points. (this circle can be 0, >>> in which case the neuron is white), for which the circle is completely >>> black >>> - when we encounter one or more white points, a direction of the >>> points is calculated. if there's no whitespace at the other side, the >>> neuron offset is moved for x/2 in the opposite direction and analyze neuron >>> is restarted for x/2. else, quit the 'analyze neuron' part. This can be >>> done in local memory, in which case it will cost you 256*2=512 bytes of >>> local ram to determine the optimal neuron position. Most graphic cards have >>> a limit of 32k ram, so this is no problem :-) >>> - determine the closest dot next to this one: >>> for each dot != this one, draw a line of black points, if no line >>> can be found, jump to next dot. >>> watch distance. If it's smaller than the previous neuron && this >>> dot id hasn't a link pointing from the destination to this one, save dot id. >>> so, at the end: >>> - each neuron of each offset is optimally centered in a return >>> matrix of 256*256*64*2 = 2²³ = 8M of memory >>> - each neuron has a unique id to its closest neighbour, to which >>> it's guaranteed to be attached. an id of -1 means no id could be found. >>> 256*256*64 = 4M of memory >>> >>> 3. we focus on neuron list -> character mapping. this is a separate >>> kernel. A "probability" factor is involved here, but I will think about it >>> further. I suggest to use a list of 64 character images at once, otherwise >>> you need lots of memory :-) >>> - define the top, left and right neuron. create a zoom factor for the >>> image. calculate the aspect ratio. The probability is >>> 1-diff(aspect_ratio1, aspect_ratio2) >>> - analyze each link in the font character. total probability *= >>> (found_link_length / total_link_length) >>> - report the probability. >>> On the PC: the character with the highest probability is the character >>> you 're looking for. Be aware that you need to compare the possibilities >>> of the different offsets if they overlap. >>> >>> if the tesseract project can use this, please let me know >>> >>> 2018-04-27 9:36 GMT+00:00 Zdenko Podobny <[email protected]>: >>> >>>> Only documentation we have is code itself ;-) But you can start with >>>> searching for opencl issue in tesseract issue tracker on github... >>>> >>>> Zdenko >>>> >>>> >>>> pi 27. 4. 2018 o 10:56 Janpieter Sollie <[email protected]> >>>> napísal(a): >>>> >>>>> I'd be glad to help. using tesseract 4, I am able to perform a 90% >>>>> accuracy on OpenCL. I do not have any experience with neural networks >>>>> (i'm >>>>> just a high-school (no college educated IT-support guy with some knowledge >>>>> about OpenCL), so can you recommend me some documentation to understand >>>>> the >>>>> engine of tesseract 4? >>>>> >>>>> 2018-04-27 10:50 GMT+02:00 Zdenko Podobny <[email protected]>: >>>>> >>>>>> If you have experience your help will be warmly welcomed. >>>>>> OpenCL is not maintained and it is on good way to be removed if >>>>>> maintainer/contributor will not be found. >>>>>> Anyway it is not used extensively, so there is a place for >>>>>> improvement, >>>>>> >>>>>> Zdenko >>>>>> >>>>>> >>>>>> pi 27. 4. 2018 o 10:21 Janpieter Sollie <[email protected]> >>>>>> napísal(a): >>>>>> >>>>>>> Hello everyone, >>>>>>> >>>>>>> I have a question about the openCL selection procedure of tesseract: >>>>>>> >>>>>>> my output: >>>>>>> >>>>>>> [DS] Profile read from file (tesseract_opencl_profile_devices.dat). >>>>>>> [DS] Device[1] 1:Fiji score is 0.202927 >>>>>>> [DS] Device[2] 1:Ellesmere score is 1.468799 >>>>>>> [DS] Device[3] 1:Ellesmere score is 1.468799 >>>>>>> [DS] Device[4] 1:Bonaire score is 1.533776 >>>>>>> [DS] Device[5] 1:Tonga score is 0.184236 >>>>>>> [DS] Device[6] 0:(null) score is 1.123015 >>>>>>> [DS] Selected Device[5]: "Tonga" (OpenCL) >>>>>>> >>>>>>> Ugh, this is weird .. why does tesseract take my Tonga instead of my >>>>>>> fiji device? can I force it to use the fiji? >>>>>>> I understand the ellesmere have lower access times (they 're behind >>>>>>> a pcie switch), but fiji and tonga are both directly connected via a >>>>>>> pcie >>>>>>> 2.0 X16 bus. Do we need a better tesseract selection procedure? >>>>>>> If so, I'm quite skilled at opencl, I'd be glad to help! >>>>>>> >>>>>>> kind regards, >>>>>>> >>>>>>> Janpieter >>>>>>> >>>>>>> -- >>>>>>> You received this message because you are subscribed to the Google >>>>>>> Groups "tesseract-ocr" group. >>>>>>> To unsubscribe from this group and stop receiving emails from it, >>>>>>> send an email to [email protected]. >>>>>>> To post to this group, send email to [email protected]. >>>>>>> Visit this group at https://groups.google.com/group/tesseract-ocr. >>>>>>> To view this discussion on the web visit >>>>>>> https://groups.google.com/d/msgid/tesseract-ocr/4568b2b8-532 >>>>>>> d-457c-920b-60407e7b278e%40googlegroups.com >>>>>>> <https://groups.google.com/d/msgid/tesseract-ocr/4568b2b8-532d-457c-920b-60407e7b278e%40googlegroups.com?utm_medium=email&utm_source=footer> >>>>>>> . >>>>>>> For more options, visit https://groups.google.com/d/optout. >>>>>>> >>>>>> -- >>>>>> You received this 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