Google API Document AI OCR

Estoy usando Document IA API de Google, realice la conexión y logre procesar algunos archivos pero necesito recuperar los puntos de referencia o coordenadas (X, Y) dentro del documento, es decir como hacer para que el campo boundingPolyForDemoFrontend de las entidades no vuelva en null. Necesito analizar un documento por su lenguaje natural recuperar estas entidades y señalar en el documento estas entidades.enter image description here Aquí en la imagen señalo lo que podría ser el valor que me devuelve las coordenadas. Saludos!

Automatic rotation of PDF documents using OCR in R

Can anyone give an idea of ​​the automatic rotation of pdf documents. I work in R.
I have documents that have rotation problems and that rotate in all directions.
I need to align them into one and I want to extract information from them.

How can I OCR text from specific regions of a screenshot and then publish it in a usable format?

I'm trying to automate the logging of all games classified in Street Fighter – Yes, cheesy, I know. I have a spreadsheet, and in that I manually put the following for each ranked game:

  1. The username of the opponent
  2. Their character
  3. Their points before the match
  4. My points before the match

It's not a huge deal, but we're on Super User, right? Ideally, I would love to be able to take a screenshot of the screen that shows this information, and then have these details somehow automatically analyzed by OCR, from One way that I can then use further down the line .. one way or the other. How can I do it? I have no idea but I am sure it is possible.

Appreciate any help!

python – Test OCR on generated images

I would recommend thinking about readimage.py& # 39; s tess() and cune() like a sort of black box that uses OCR. Anyway, this code is intended to be used for a science fair project where I am testing Tesseract and Cuneiforms' abilities to read text on images with different font sizes and colors, etc. Any thoughts, obvious mistakes, etc.?

main.py:

# Command-line arguments and other functionalities
import os
import sys
import math
import random
import ast
import argparse

# Image handling and OCR
import readimage
import drawimage
import distance

# Constants
DIMENSIONS = (850, 1100, 50, 50) # Width, Height, Side Margin, Top Margin
DICTLOC = "dict.txt"
COLORS = {
    "R" : ((255,0,0), "Red"),
    "G" : ((0,255,0), "Green"),
    "W" : ((255,255,255), "White"),
    "B" : ((0,0,0), "Black"),
    "Y1" : ((255,252,239), "Yellow1"),
    "Y2" : ((255,247,218), "Yellow2"),
    "Y3" : ((255,237,176), "Yellow3"),
    "Y4" : ((255,229,139), "Yellow4"),
}

# Read command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument("-p", "--pages", type=int, help="Pages per Setting", default=1)
parser.add_argument("-f", "--fonts", help="Comma-Seperated List of fonts", default="freefont/FreeMono.ttf")
parser.add_argument("-tc", "--txtcolors", help="Comma-Seperated Color Initials", default="B")
parser.add_argument("-bc", "--bgcolors", help="Comma-Seperated Color Initials", default="W")
parser.add_argument("-hs", "--headsizes", type=str, help="Comma-Seperated Header Font Heights", default="50")
parser.add_argument("-bs", "--bodysizes", type=str, help="Comma-Serperated Body Font Heights", default="25")
parser.add_argument("-v", "--verbose", help="Print progress", action="store_true")
args = parser.parse_args()
pages = args.pages
fonts = args.fonts.split(",")
txtcolors = (COLORS(c) for c in args.txtcolors.split(","))
bgcolors = (COLORS(c) for c in args.bgcolors.split(","))
headsizes = (int(s) for s in args.headsizes.split(","))
bodysizes = (int(s) for s in args.bodysizes.split(","))
verbose = args.verbose

# Grab dictionary as list of words
worddict = open(DICTLOC).read()
worddict = worddict.split("n")

def image_stats(file, correct, language="eng", tessconfig=""):
    tess = {}
    tess_out, tess("time") = readimage.tess_ocr("img.png")
    tess_out = " ".join(tess_out.split()).strip()
    tess("dist") = distance.lev(correct, tess_out)
    tess("per") = round((len(correct)-tess("dist"))/len(correct),4)
    tess("tpc") = round(tess("time")/len(correct)*1000, 4)

    cune = {}
    cune_out, cune("time") = readimage.cune_ocr("img.png")
    cune_out = " ".join(cune_out.split()).strip()
    cune("dist") = distance.lev(correct, cune_out)
    cune("per") = round((len(correct)-cune("dist"))/len(correct),4)
    cune("tpc") = round(cune("time")/len(correct)*1000, 4)
    return tess, cune

def main():
    if os.path.exists("fullout.txt"):
        os.remove("fullout.txt")
    if os.path.exists("avgout.txt"):
        os.remove("avgout.txt")
    fullout = open("fullout.txt",mode='a')
    fullout.write("tCuneiformtTesseracttCuneiformtTesseracttCuneiformtTesseracttCuneiformtTesseractn")
    avgout = open("avgout.txt", mode='a')
    avgout.write("tCuneiformtTesseracttCuneiformtTesseracttCuneiformtTesseracttCuneiformtTesseractn")
    for font in fonts:
        for txtcolor in txtcolors:
            for bgcolor in bgcolors:
                fullout.write(f"Font: {font}, {txtcolor(1)} on {bgcolor(1)}tCuneiformtTesseracttCuneiformtTesseracttCuneiformtTesseracttCuneiformtTesseractn")
                avgout.write(f"Font: {font}, {txtcolor(1)} on {bgcolor(1)}tCuneiformtTesseracttCuneiformtTesseracttCuneiformtTesseracttCuneiformtTesseractn")
                for headsize in headsizes:
                    for bodysize in bodysizes:
                        cune_stats = ()
                        tess_stats = ()
                        avgout.write(f"{bodysize}")
                        for page in range(pages):
                            fullout.write(f"{bodysize}")
                            title = drawimage.generate_words(worddict, random.randint(1,10))
                            body = drawimage.generate_words(worddict, 10000)
                            img, correct = drawimage.create_page(title, body, DIMENSIONS, txtcolor(0), bgcolor(0), headsize, bodysize, font)
                            img.save("img.png")
                            correct = " ".join(correct).replace("n", " ")
                            tess, cune = image_stats("img.png", correct)
                            tess_stats.append(tess)
                            cune_stats.append(cune)
                            fullout.write(f"t{cune('time')}t{tess('time')}t{cune('tpc')}t{tess('tpc')}t{cune('dist')}t{tess('dist')}t{cune('per')}t{tess('per')}n")
                        cune = {}
                        tess = {}
                        for stat in cune_stats(0):
                            cune(stat) = round(sum((i(stat) for i in cune_stats)) / len(cune_stats), 4)
                            tess(stat) = round(sum((i(stat) for i in tess_stats)) / len(tess_stats), 4)
                        avgout.write(f"t{cune('time')}t{tess('time')}t{cune('tpc')}t{tess('tpc')}t{cune('dist')}t{tess('dist')}t{cune('per')}t{tess('per')}n")
    fullout.close()
    avgout.close()
if __name__ == "__main__":
    main()

drawimage.py:

from PIL import Image, ImageDraw, ImageFont
import random

# Turn words into lines, based on size of page and font, then return lines and height of lines
def word_space(words, font, height, spaceh=30):
    linew = 0
    linet = "" 
    lines = ()
    wordnum = 0
    while wordnum < len(words):
        if len(linet) > 0: linet += " " 
        linet += words(wordnum)
        if font.getsize(linet)(0) > DIMENSIONS(0) - (2*DIMENSIONS(2)):
            if spaceh * (len(lines)+1) > height:
                linet = linet(:-(len(words(wordnum))+1))
                break
            else:
                linet = linet(:-(len(words(wordnum))+1))
                if font.getsize(words(wordnum))(0) > DIMENSIONS(0) - (2*DIMENSIONS(2)):
                    print("Word too long, skipping: " + words(wordnum))
                    wordnum += 1
                else:
                    lines.append(linet)
                    linet = ""
        else:
            wordnum += 1
    if linet:
        lines.append(linet)

    return lines, spaceh * len(lines)


# Add text to an image, return new image
def add_text(img, text, pos, font, fcolor):

    d = ImageDraw.Draw(img)
    d.text(pos, text, font=font, fill=fcolor)
    return img

# Draw an entire page, return image and correct text
def create_page(title, body, DIM, txtcolor, bgcolor, titlesize, bodysize, font):
    global DIMENSIONS
    DIMENSIONS = DIM
    img = Image.new('RGBA', (DIMENSIONS(0), DIMENSIONS(1)), bgcolor+(255,))
    titlefont = ImageFont.truetype(font, titlesize)
    bodyfont = ImageFont.truetype(font, bodysize)
    titlespaced, titleh = word_space(title, titlefont, DIMENSIONS(1)-40, spaceh=titlesize+10)
    for i, line in enumerate(titlespaced):
        img = add_text(img, line, (50,(titlesize+10)*i+20),titlefont,txtcolor)
    bodyspaced, margin = word_space(body, bodyfont, DIMENSIONS(1)-40-titleh-20, spaceh=bodysize+10)
    for i, line in enumerate(bodyspaced):
        img = add_text(img, line, (50,((bodysize+10)*i)+titleh+20), bodyfont, txtcolor)
    return img, titlespaced+bodyspaced

# Generate and return a given number of words
def generate_words(worddict, length):
    words = ()
    for j in range(length):
        word = random.choice(worddict)
        mod = random.randint(1,10)
        if mod == 1:
            word = word.upper()
        elif mod == 2:
            word = word.capitalize()
        elif random.randint(1,15) == 1:
            word += "."
        words.append(word)
    return words

readimage.py:

import subprocess
import os
import time
from PIL import Image
import pytesseract

# Functions to run either OCR on a given image.

def tess_ocr(file, language="eng", config=""):
    # Run and time Tesseract, return output
    start = time.time()
    out = pytesseract.image_to_string(Image.open(file), language, config=config)
    return out, round(time.time() - start, 4)

def cune_ocr(file, language="eng"):
    # Run Cuneiform on image
    start = time.time()
    subprocess.call(("cuneiform", "-o", "cuneout.txt",file), stdout=subprocess.PIPE)

    # Fetch and return output
    if os.path.exists("cuneout.txt"):
        out = open("cuneout.txt").read()
        return out, round(time.time() - start, 4)
    else:
        print("Cuneiform reported no output, returning empty string")
        return "", round(time.time() - start, 4)

distance.py:

import numpy

# Find Levenshtein Distance between two strings
def lev(a,b):
    sizex = len(a)+1
    sizey = len(b)+1
    matrix = numpy.zeros((sizex,sizey))
    for x in range(sizex):
        matrix(x,0) = x
    for y in range(sizey):
        matrix(0,y) = y

    for y in range(1, sizey):
        for x in range(1, sizex):
            cost = 0
            if a(x-1) != b(y-1):
                cost = 2
            matrix(x,y) = min(
                matrix(x-1,y) + 1,
                matrix(x,y-1) + 1,
                matrix(x-1,y-1) + cost
            )

    return int(matrix(sizex-1,sizey-1))

Windows App – ORPALIS PDF OCR 1.1.29 Professional | NulledTeam UnderGround

Languages: English, French | Size of the file: 195.08 MB

Turn all your documents into searchable PDF files! Scanned documents and images can now be viewed in the blink of an eye thanks to an innovative conversion engine. If you need a simple way to convert them into searchable documents, the use of third-party software solutions may be the best alternative. ORPALIS PDF OCR is one of the programs that can help you easily accomplish the above mentioned task.
A flawless stay

Why PDF OCR?

To provide a fast and powerful tool, it takes a lot of technology. Here are some facts about ORPALIS PDF OCR and the team that developed it.

– Tool for faster conversion of documents into PDF OCR of the market.
– Optical character recognition and high quality presentation analysis.
– Productive and intuitive interface.
– Image files are now searchable
– No more lost time searching for information in log documents.
– Performs fast automatic indexing on a high volume of documents.
– User-friendly software thanks to its intuitive interface.
– Fast and reliable OCR engine, optimized by the GdPicture.NET SDK, bestseller all over the world.
– Built by recognized experts from the industry.

Discover the innovative features of OCR PDF:

Input file formats
Convert PDF (PDF OCR Cloud Edition) and more than 100 other file formats (PDF OCR On-Premise Edition) into a searchable PDF!

Supported languages
PDF OCR On-Site Edition supports more than 60 languages! The Cloud Edition includes English, French, Spanish, German and Italian.

Multithreading support for a multipage document
Powerful PDF OCR Multi-Threading Engine can handle very long documents and hundreds of pages at a time!

Multi Threading
Multithreading support for multiple documents.

Command line support
Integrate all the features of PDF OCR into your production line, automate your processes and save a lot of time!

Analysis of the layout
With this feature, the orientation of each page is automatically detected to provide the most accurate OCR result possible.

Selection of documents
You can select the exact document that will be processed by PDF OCR or the whole folder. Select your files or folders or drag them directly into OCR PDF.

Localized user interface
At the moment, the user interface is translated into English and French, but hey, other languages ​​are coming!

64-bit support
PDF OCR is AnyCPU, which means that the application runs as much as possible as a 64-bit process and drops back to 32-bit when only that mode is available.

VERSION NOTES:
– Improved accuracy and speed of the OCR engine.

requirements: Windows from XP SP3 to Windows 10.

HOME PAGE

Download from UploadGig

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A flawless stay

How to prevent Google Drive OCR from scanning your image files?

I realized that google drive image files are "scanned by ocr" to search for words (letters, numbers).
Because they then appear in Google Drive search results …
Is it possible to disable this & # 39; ocr scan & # 39; and this automatic indexing? For example for security reasons …

c # – Designing an OCR solution with .NET

I am new to software design and development.

I'm trying to develop an OCR service using the .NET Framework that can be used by different projects that we have in .NET and other frameworks.
In the service, I use the Iron OCR library to perform the OCR.
Here are the important steps of my OCR service:

  1. My service would be able to accept documents, PDF and Excel
    files.

  2. Detect the language of the input file (using Microsoft Translate Text APIs)

  3. Implement the corresponding language pack from the Iron OCR Library

  4. Perform the OCR using the IRON OCR library and convert the result
    text to a
    document or a PDF.

I want to make this solution resizable and adaptable. What are the things I should look for.

What should be the architecture of my project? The solution I currently have does not support OCR and is a SPA application from which users download files, then I press a REST-based API that also calls Microsoft Cognitive Services to translate documents and resend them by email to the user.

OCR in Android Studio text by text

Currently, I am developing software and I need to know how to use optical character recognition (OCR). Is there a link or video that can provide me with valid information about OCR?

Mac App – Cisdem PDF Converter OCR 6.8.0 macOS | NulledTeam UnderGround

Cisdem PDF Converter OCR 6.8.0 | macOS | 362 mb

Cisdem PDF OCR Converter for Mac will help you convert any native PDF, scanned PDF and PDF encrypted into PDF, Word, Text, Excel, PPT, ePub, HTML, Text, RTFD, Pages, Keynote and images editable and searchable (JPEG, BMP, PNG, GIF, TIFF) with its OCR technology while maintaining the layout and quality of the original file.

Is ReverseProxies OCR still there?

I suppose not …. : |