Recognize text from picture

mikael

@Spitfire, thanks. I did try all kinds of approaches, finally resorting to manual cropping, and it still was not reliable enough.

pavlinb

@mikael Could you share some picture of sudoku, where recognition fails?

ccc

Multiple sample Sudoku puzzles would help to achieve a robust solution.

sodoku

I have a few questions about the very first text recognition code posted on this one

the example video I am referring to is https://developer.apple.com/videos/play/wwdc2019/234

1 how do you change the recognition level from fast to accurate
example code from apple website I am not sure if its written in swift or objective c but it is like this :

myTextRegcognitionRequest.recognitionLevel = VNRequestTextRecognitionLevel.accurate

and another example of this shown in the apple video for setting the recognition level

 request.recognitionLevel = .fast

question 2
to ensure that numbers don't get mistaken as letters
without the language corrector active to avoid mistaking the number 5 for an S or I as 1
example of this from the video is

extension Character {
       
     func GetSimilarCharacterIfNotIn(allowedChars: String -> Character {
            let  conversionTable = [
                      's':'5',
                      'S':'5',
                      'i':'1',
                      'I':'1', ]

question 3
if you know how to set up the special words detector thingy feature mentioned in the video

westjensontexas

I gather they are looking for words you'd find in an English dictionary. So perhaps façade, or tête-à-tête might recognize, while other examples wouldn't? mobdro apk tubemate

JonB

@sodoku
See https://developer.apple.com/documentation/vision/vnrequesttextrecognitionlevel/fast

Try req.recognitionLevel=1 for fast, or 0 for accurate.

Re fixing characters... I gather you might set req.usesLanguageCorrection=False (or maybe 0), then make your own replacement map and use str.translate.

Custom words is handled by
req.customWords = ['customword1', 'etc']

See apple docs for VNRecognizeTextRequest

sodoku

ive seen the apple documentation coding on Vision Framework I just dont know how to convert it to python

Question 1
What about the setting the minimum text height how do you translate either of these codes to python????
@property(readwrite, nonatomic, assign) float minimumTextHeight; written in objective-c
var minimumTextHeight: Float { get set } written in Swift

Question 2
I was also interested in learning how to recognize the individual boxes from a sudoku puzzle to extract the numbers is there a way to do that possibly with
VNRecognizedTextObservation A request that detects and recognizes regions of text in an image.
or possibly with the bounding box technique show in the video https://developer.apple.com/videos/play/wwdc2019/234 , also can you put multiple bounding boxes to recognize text from a sudoku card

this is Mikeals code i am trying to insert the code into but dont know how to convert the code shown in the apple documentation into python

language_preference = ['fi','en','se']

import photos, ui, dialogs
import io
from objc_util import *

load_framework('Vision')
VNRecognizeTextRequest = ObjCClass('VNRecognizeTextRequest')
VNImageRequestHandler = ObjCClass('VNImageRequestHandler')

def pil2ui(pil_image):
    buffer = io.BytesIO()
    pil_image.save(buffer, format='PNG')
    return ui.Image.from_data(buffer.getvalue())

selection = dialogs.alert('Get pic', button1='Camera', button2='Photos')

ui_image = None

if selection == 1:
    pil_image = photos.capture_image()
    if pil_image is not None:
        ui_image = pil2ui(pil_image)
elif selection == 2:
    ui_image = photos.pick_asset().get_ui_image()

if ui_image is not None:
    print('Recognizing...\n')

    req = VNRecognizeTextRequest.alloc().init().autorelease()
    req.recognitionLevel=1
    req.setRecognitionLanguages_(language_preference)
    handler = VNImageRequestHandler.alloc().initWithData_options_(ui_image.to_png(), None).autorelease()

    success = handler.performRequests_error_([req], None)
    if success:
        for result in req.results():
            print(result.text())
    else:
        print('Problem recognizing anything')

JonB

@sodoku For things like enumerations, you can usually check the swift version of docs, which tells you the value. Otherwise, you can often look up source code.

For minimumTextHeight, both swift and ObjC say this is a float. The fact that it is readwrite/nonatomic/assign is not important.
So, usually this would just be
req.minimumTextHeight = 32.5
or whatever you want...

It is often helpful to explore objects in the console, since this can tell you what you're working with. For instance, if you type req. in the console, you will see autocomplete of all known attributes. Usually you need to treat objc properties as function calls -- so to check minimumTextHeight, you'd use req.minimumTextHeight(). But to set, you can treat the property as a python attribute and assign directly. In some cases, you may need to use the set_propertyName_(value) convention.

Where things get tricky is where the declared type is another object (in which case you have to provide the right type of object), or a structure. Structures can be tricky because objc_util often screws up the type encodings, and you have to manually override. Structures get turned into python STRUCTUREs, and you access fields normally like you would with a python object (no () needed).

Re question 2:
Per the docs, the results of a request will be VNRecognizedTextObservation objects. This is a subclass of VNRectangleObservation.

@interface VNRecognizedTextObservation : VNRectangleObservation <-- colon here means inherits from

If you look up VNRectangleObservation, you will see it has the following attributes
bottomLeft
bottomRight
topLeft
topRight
Which are declared as CGPoint, which is a structure that has an .x and .y fields.

for result in req.results():
    x = result.bottomLeft().x
    y = result.bottomLeft().y
    w = result.topRight().x-x
    h = result.topRight().y-y
    print('({},{},{},{}) {}'.format(x,y,w,h, result.text())

You could draw the image into an image context, and then also stroke a rectangle.. something like this...(not tried).

with ui.ImageContext(ui_image.size()) as ctx:
   ui_image.draw()
   for result in req.results():
      vertecies = [(p.x, p.y) 
                               for p in [result.bottomLeft()
                                        result.TopLeft()
                                        result.TopRight()
                                        result.BottomRight()
                                        result.bottomLeft()]
      pth = ui.Path.moveTo(*vertecies[0]) %initial point
      for p in vertecies[1:]:
         pth.line_to(*p)  
      ui.set_color('red')
      pth.stroke()
      x,y = vertecies[0]
      w,h =(vertecies[2].x-x), (vertecies[2].y-y)
      ui.draw_string(result.text(), rect=(x,y,w,h), font=('<system>', 12), color='red')
   marked_img = ctx.get_image()
   marked_img.show()

JonB

I realized that result will also have a .boundingBox() attribute which would make some of this a little simpler.
That is a CGrect, consisting of .origin (in turn consisting of .x and .y and .size containing .w and .h.
In that case you could use ui.Path.rect.

JonB

Okay, my previous reply was full of errors... here is a working version, which adds red boxes around each result, along with the text

language_preference = ['fi','en','se']

import photos, ui, dialogs
import io
from objc_util import *

load_framework('Vision')
VNRecognizeTextRequest = ObjCClass('VNRecognizeTextRequest')
VNImageRequestHandler = ObjCClass('VNImageRequestHandler')

ACCURATE=0
FAST=1

def pil2ui(pil_image):
    buffer = io.BytesIO()
    pil_image.save(buffer, format='PNG')
    return ui.Image.from_data(buffer.getvalue())

selection = dialogs.alert('Get pic', button1='Camera', button2='Photos')

ui_image = None

if selection == 1:
    pil_image = photos.capture_image()
    if pil_image is not None:
        ui_image = pil2ui(pil_image)
elif selection == 2:
    ui_image = photos.pick_asset().get_ui_image()

if ui_image is not None:
    print('Recognizing...\n')

    req = VNRecognizeTextRequest.alloc().init().autorelease()
    req.recognitionLevel= ACCURATE# accurate
    req.setRecognitionLanguages_(language_preference)
    handler = VNImageRequestHandler.alloc().initWithData_options_(ui_image.to_png(), None).autorelease()

    success = handler.performRequests_error_([req], None)
    if success:
        for result in req.results():
            print(result.text())
    else:
        print('Problem recognizing anything')

with ui.ImageContext(*tuple(ui_image.size) ) as ctx:
   ui_image.draw()
   for result in req.results():
      cgpts=[   result.bottomLeft(),
                result.topLeft(),
                result.topRight(),
                result.bottomRight(),
                result.bottomLeft()  ] 
      vertecies = [(p.x*ui_image.size.w, (1-p.y)*ui_image.size.h) for p in cgpts]
      pth = ui.Path()
      pth.move_to(*vertecies[0]) 
      for p in vertecies[1:]:
         pth.line_to(*p)  
      ui.set_color('red')
      pth.stroke()
      x,y = vertecies[0]
      w,h =(vertecies[2][0]-x), (vertecies[2][1]-y)
      ui.draw_string(str(result.text()), rect=(x,y,w,h), font=('<system>', 12), color='red')
   marked_img = ctx.get_image()
   marked_img.show()

mikael

@JonB and @sodoku, just a note that I tried a different route, where I first used a rectangle recognizer to isolate the numbers, and only then used text recognition.. The results were not impressive, but I can try to find the code for reference, if you think it might be useful.

JonB

Here is another solution... I use a rectangle detection and a perspective correction to crop the puzzle. This gives much better detection, though not perfect. The recognition is pretty good, though it has troubles with 1’s on their own.... turn into Ts of all things. Some additional work in the clean function might fix common problems.

I’m using images from https://github.com/prajwalkr/SnapSudoku/tree/master/train

I suspect doing some CIFiltering first will probably improve things.

from objc_util import *
import ui

VNImagePointForNormalizedPoint=c.VNImagePointForNormalizedPoint
VNImagePointForNormalizedPoint.argTypes=[CGPoint, c_int, c_int]
VNImagePointForNormalizedPoint.restype=CGPoint


ui_image=ui.Image.named('image2.jpg')
ui_image.show()

CIImage=ObjCClass('CIImage')
ci_image=CIImage.imageWithCGImage_(ui_image.objc_instance.CGImage())

CIPerspectiveCorrection=ObjCClass('CIPerspectiveCorrection')
f=CIPerspectiveCorrection.perspectiveCorrectionFilter()
f.inputImage=ci_image
o=f.outputImage()

load_framework('Vision')
VNRecognizeTextRequest = ObjCClass('VNRecognizeTextRequest')
VNDetectRectanglesRequest = ObjCClass('VNDetectRectanglesRequest')
VNImageRequestHandler = ObjCClass('VNImageRequestHandler')

req=VNDetectRectanglesRequest.alloc().init().autorelease()
req.maximumObservations=2
req.minimumSize=0.5
req.minimumAspectRatio=0.7
req.quadratureTolerance=30

handler = VNImageRequestHandler.alloc().initWithData_options_(ui_image.to_png(), None).autorelease()

success = handler.performRequests_error_([req], None)
try:
   result=req.results()[0]
   nm=lambda p :VNImagePointForNormalizedPoint(p,int(ui_image.size.w),int(ui_image.size.h))
   f.topLeft = nm(result.topLeft())
   f.topRight = nm(result.topRight())
   f.bottomLeft = nm(result.bottomLeft())
   f.bottomRight = nm(result.bottomRight())
   o=f.outputImage()

   with ui.ImageContext(o.extent().size.width, o.extent().size.height) as ctx:
     UIImage.imageWithCIImage_(o).drawAtPoint_( CGPoint(0,0))
     ui_image2=ctx.get_image()
   ui_image2.show()
except:
   print('bounding rec not found...results wont work')
   ui_image2=ui_image
'''now, detect rectangles again...'''
handler = VNImageRequestHandler.alloc().initWithData_options_(ui_image2.to_png(), None).autorelease()
req0 = VNRecognizeTextRequest.alloc().init().autorelease()
req0.recognitionLevel= 0# accurate
req0.usesLanguageCorrection=True
req0.customWords=[str(a) for a in range(10)]

#req0.maximumObservations=81
#req0.minimumSize=.1
success = handler.performRequests_error_([req0], None)
with ui.ImageContext(*tuple(ui_image2.size) ) as ctx:
   ui_image2.draw()
   for result in req0.results():
      cgpts=[result.bottomLeft(),
                                        result.topLeft(),
                                        result.topRight(),
                                        result.bottomRight(),
                                        result.bottomLeft()] 
      vertecies = [(p.x*ui_image2.size.w, (1-p.y)*ui_image2.size.h) for p in cgpts]
      pth = ui.Path()
      pth.move_to(*vertecies[0]) 
      for p in vertecies[1:]:
         pth.line_to(*p)  
      ui.set_color('red')
      pth.stroke()
      x,y = vertecies[0]
      w,h =(vertecies[2][0]-x), (vertecies[2][1]-y)

      ui.draw_string(str(result.text()), rect=(x,y,w,h), font=('<system>', 12), color='red')
   marked_img = ctx.get_image()
   marked_img.show()
   
def bbcenter(bb):
   return((9*(bb.origin.x+bb.size.width/2)-0.5), 
          (9*(bb.origin.y+bb.size.height/2)-0.5) )
def clean(results):
   cleaned=[]
   for r in results:
      col,row=bbcenter(r.boundingBox())
      approx_num_ch=(r.boundingBox().size.width*9)
      txt=str(r.text()).replace(' ','')
      if approx_num_ch<=1:
         if len(txt) == 1:
             cleaned.append(((round(col),round(row)),txt))
         else:
             cleaned.append(((round(col),round(row)),'-1'))
      else: #more than one char
         col-=(len(txt)-1)/2
         col=round(col)
         row=round(row)
         for ch in txt:
           if ch in [str(a) for a in range(10)]:
             cleaned.append(((col,row),ch))
           else:
             cleaned.append(((col,row),'-1'))
           col+=1
   return cleaned


import numpy as np
puzzle=np.zeros([9,9])
for c,v in clean(req0.results()):
   puzzle[c]=int(v)
print(np.flipud(puzzle.T))

mikael

@JonB, thanks, very nice. I have noted and wondered about how difficult number 1 is to recognize... Not very exotic, is it? But in my experiments it looked like the simple heuristic of ”if the result is something else than 1-9, assume it is a 1” would work pretty well for Sudoku.

mikael

@JonB, can you open up this one a little bit?

approx_num_ch=(r.boundingBox().size.width*9)

JonB

The *9 is because if the initial rectangle detection and crop works, then each square is approx 1/9 width. So the approx number of squares a rectangle covers tells us how many characters it should have... I was getting many cases where 1 got read as Te, or some other two character value, even though the width was less than one box... so I wanted to have special handling for narrow boxes, as that is probably a 1, while wide boxes could have multiple characters because the bounding box legitimately spans adjacent boxes.

mikael

@JonB, there’s something in the math here I do not quite get. I would expect something like:

num_char = r.bbox/(full_bbox/9) = r.bbox * 9 / full_bbox

Thus looks like you are missing the division?

JonB

The results of vision are always provided as normalized coordinates — meaning the full box is always 1.
For drawing, you have to then multiply by image width/height.

Since the perspective correction both fixes perspective and crops — 1/9 is the size, roughly, of a single cell.

mikael

@JonB, now I understand, thank you.

sodoku

I have a quick question in regards to the original ocr post how do I print the text as one single csv list, I tried but I have been getting a list of lists instead of one single list

This is a snippet of the code example I think needs to be altered

  success = handler.performRequests_error_([req], None)
    if success:
        for result in req.results():
            print(result.text())
    else:
        print('Problem recognizing anything')

JonB

results=[str(result.text()) for result in req.results()]

print(results)

Or maybe

print(','.join(results))