Condense 1 5 – Optical Character Recognition (ocr) Appl...

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Condense 1 5 – Optical Character Recognition (ocr) Application

Condense 1.5 released! Lot's of new features, ready for Yosemite!Typing out has come to an end. Say ‚Hello‘ to Condense. Your easy to use optical character recognition (OCR) application.Imagine one of the following scenarios:You're writing an essay for University and you've to.
The following applications support OCR plugin to capture text via camera: - Online and Offlline Dictionaries by Livio, version 3.5 or later - Online Thesaurus by Livio, version 3.5 or later Information for Android application developers: This application provides Android application interface for 3rd party applications, please read further.
OCR (Optical Character Recognition) software offers you the ability to use document scanning of scan invoices, text, and other files into digital formats - especially PDF - in order to make it.
What Is Optical Character Recognition
Optical Character Recognition Ocr Free
Condense 1 5 – Optical Character Recognition (ocr) Application Tool
OCR or Optical Character Recognition is a sophisticated software technique that allows a computer to extract text from images. In the early days OCR software was pretty rough and unreliable. Now, with the tons of computing power on tap, it's often the fastest way to convert text in an image into something you can edit with a word processor.
Practical OCR ApplicationsIn recent years, OCR (Optical Character Recognition) technology has been applied throughout the entire spectrum of industries, revolutionizing the document management process. OCR has enabled scanned documents to become more than just image files, turning into fully searchable documents with text content that is recognized by computers. With the help of OCR, people no longer need to manually retype important documents when entering them into electronic databases. Instead, OCR extracts relevant information and enters it automatically. The result is accurate, efficient information processing in less time.
Banking The uses of OCR vary across different fields. One widely known OCR application is in banking, where OCR is used to process checks without human involvement. A check can be inserted into a machine, the writing on it is scanned instantly, and the correct amount of money is transferred. This technology has nearly been perfected for printed checks, and is fairly accurate for handwritten checks as well, though it occasionally requires manual confirmation. Overall, this reduces wait times in many banks. 
Read More on How CVISION Revolutionizes the Document Management Process for Organizations in the Banking and Financial Services
Legal In the legal industry, there has also been a significant movement to digitize paper documents. In order to save space and eliminate the need to sift through boxes of paper files, documents are being scanned and entered into computer databases. OCR further simplifies the process by making documents text-searchable, so that they are easier to locate and work with once in the database. Legal professionals now have fast, easy access to a huge library of documents in electronic format, which they can find simply by typing in a few keywords. 
CVISION Simplifies the Document Processing for Organizations in the Legal Industry
Healthcare Healthcare has also seen an increase in the use of OCR technology to process paperwork. Healthcare professionals always have to deal with large volumes of forms for each patient, including insurance forms as well as general health forms. To keep up with all of this information, it is useful to input relevant data into an electronic database that can be accessed as necessary. Form processing tools, powered by OCR, are able to extract information from forms and put it into databases, so that every patient's data is promptly recorded. As a result, healthcare providers can focus on delivering the best possible service to every patient. 
CVISION Creates A More Efficient Document Processing for Numerous Organization in the Healthcare Industry
OCR in Other Industries OCR is widely used in many other fields, including education, finance, and government agencies. OCR has made countless texts available online, saving money for students and allowing knowledge to be shared. Invoice imaging applications are used in many businesses to keep track of financial records and prevent a backlog of payments from piling up. In government agencies and independent organizations, OCR simplifies data collection and analysis, among other processes. As the technology continues to develop, more and more applications are found for OCR technology, including increased use of handwriting recognition. Furthermore, other technologies related to OCR, such as barcode recognition, are used daily in retail and other industries. To learn more about OCR solutions for your office, you can download a free trial of Maestro Recognition Server, CVISION's OCR toolkit, or request a free trial of Trapeze, our automated form-processing solution. 
Click to Read More on How CVISION Simplifies the Document Processing in the Other Industries
Further Information on Document Management SolutionsCannon fodder v2 0 0 15 download free. ICR Explained
 Automated Data Entry 
 Convert to PDF 
 PDF Compression 
 Forms Processing 
 Document Archiving 
 PDF Basics and Optimization Solutions 
Goal In this chapter:
We will use our knowledge on kNN to build a basic OCR (Optical Character Recognition) application.
We will try our application on Digits and Alphabets data that comes with OpenCV.
OCR of Hand-written Digits Our goal is to build an application which can read handwritten digits. For this we need some training data and some test data. OpenCV comes with an image digits.png (in the folder opencv/samples/data/) which has 5000 handwritten digits (500 for each digit). Each digit is a 20x20 image. So our first step is to split this image into 5000 different digit images. Then for each digit (20x20 image), we flatten it into a single row with 400 pixels. That is our feature set, i.e. intensity values of all pixels. It is the simplest feature set we can create. We use the first 250 samples of each digit as training data, and the other 250 samples as test data. So let's prepare them first. 
import cv2 as cv
img = cv.imread('digits.png')
# Now we split the image to 5000 cells, each 20x20 size
cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]
# Make it into a Numpy array: its size will be (50,100,20,20)
train = x[:,:50].reshape(-1,400).astype(np.float32) # Size = (2500,400)
test = x[:,50:100].reshape(-1,400).astype(np.float32) # Size = (2500,400)

Condense 1 5 – optical character recognition (ocr) applications

# Create labels for train and test data
train_labels = np.repeat(k,250)[:,np.newaxis]
# Initiate kNN, train it on the training data, then test it with the test data with k=1
knn.train(train, cv.ml.ROW_SAMPLE, train_labels)
ret,result,neighbours,dist = knn.findNearest(test,k=5)
# Now we check the accuracy of classification
# For that, compare the result with test_labels and check which are wrong
correct = np.count_nonzero(matches)
print( accuracy )
Touchcopy 16 21 download free. So our basic OCR app is ready. This particular example gave me an accuracy of 91%. One option to improve accuracy is to add more data for training, especially for the digits where we had more errors.
Instead of finding this training data every time I start the application, I better save it, so that the next time, I can directly read this data from a file and start classification. Torchlight 1 15. This can be done with the help of some Numpy functions like np.savetxt, np.savez, np.load, etc. Please check the NumPy docs for more details. 
np.savez('knn_data.npz',train=train, train_labels=train_labels)
# Now load the data
print( data.files )
 train_labels = data['train_labels']
 In my system, it takes around 4.4 MB of memory. Since we are using intensity values (uint8 data) as features, it would be better to convert the data to np.uint8 first and then save it. It takes only 1.1 MB in this case. Then while loading, you can convert back into float32.
OCR of the English Alphabet Next we will do the same for the English alphabet, but there is a slight change in data and feature set. Here, instead of images, OpenCV comes with a data file, letter-recognition.data in opencv/samples/cpp/ folder. If you open it, you will see 20000 lines which may, on first sight, look like garbage. Actually, in each row, the first column is a letter which is our label. The next 16 numbers following it are the different features. These features are obtained from the UCI Machine Learning Repository. You can find the details of these features in this page.
There are 20000 samples available, so we take the first 10000 as training samples and the remaining 10000 as test samples. We should change the letters to ascii characters because we can't work with letters directly. 
import numpy as np
# Load the data and convert the letters to numbers
data= np.loadtxt('letter-recognition.data', dtype= 'float32', delimiter = ',',
# Split the dataset in two, with 10000 samples each for training and test sets
What Is Optical Character Recognition# Split trainData and testData into features and responses
labels, testData = np.hsplit(test,[1])
# Initiate the kNN, classify, measure accuracy
knn.train(trainData, cv.ml.ROW_SAMPLE, responses)
ret, result, neighbours, dist = knn.findNearest(testData, k=5)
correct = np.count_nonzero(result labels)
print( accuracy )

# Create labels for train and test data
train_labels = np.repeat(k,250)[:,np.newaxis]
# Initiate kNN, train it on the training data, then test it with the test data with k=1
knn.train(train, cv.ml.ROW_SAMPLE, train_labels)
ret,result,neighbours,dist = knn.findNearest(test,k=5)
# Now we check the accuracy of classification
# For that, compare the result with test_labels and check which are wrong
correct = np.count_nonzero(matches)
print( accuracy )
Touchcopy 16 21 download free. So our basic OCR app is ready. This particular example gave me an accuracy of 91%. One option to improve accuracy is to add more data for training, especially for the digits where we had more errors.
Instead of finding this training data every time I start the application, I better save it, so that the next time, I can directly read this data from a file and start classification. Torchlight 1 15. This can be done with the help of some Numpy functions like np.savetxt, np.savez, np.load, etc. Please check the NumPy docs for more details. 
np.savez('knn_data.npz',train=train, train_labels=train_labels)
# Now load the data
print( data.files )
 train_labels = data['train_labels']
 In my system, it takes around 4.4 MB of memory. Since we are using intensity values (uint8 data) as features, it would be better to convert the data to np.uint8 first and then save it. It takes only 1.1 MB in this case. Then while loading, you can convert back into float32.
OCR of the English Alphabet Next we will do the same for the English alphabet, but there is a slight change in data and feature set. Here, instead of images, OpenCV comes with a data file, letter-recognition.data in opencv/samples/cpp/ folder. If you open it, you will see 20000 lines which may, on first sight, look like garbage. Actually, in each row, the first column is a letter which is our label. The next 16 numbers following it are the different features. These features are obtained from the UCI Machine Learning Repository. You can find the details of these features in this page.
There are 20000 samples available, so we take the first 10000 as training samples and the remaining 10000 as test samples. We should change the letters to ascii characters because we can't work with letters directly. 
import numpy as np
# Load the data and convert the letters to numbers
data= np.loadtxt('letter-recognition.data', dtype= 'float32', delimiter = ',',
# Split the dataset in two, with 10000 samples each for training and test sets
What Is Optical Character Recognition# Split trainData and testData into features and responses
labels, testData = np.hsplit(test,[1])
# Initiate the kNN, classify, measure accuracy
knn.train(trainData, cv.ml.ROW_SAMPLE, responses)
ret, result, neighbours, dist = knn.findNearest(testData, k=5)
correct = np.count_nonzero(result labels)
print( accuracy )
 It gives me an accuracy of 93.22%. Again, if you want to increase accuracy, you can iteratively add more data.
Optical Character Recognition Ocr FreeAdditional Resources Exercises Condense 1 5 – Optical Character Recognition (ocr) Application ToolHere we used k=5. What happens if you try other values of k? Can you find a value that maximizes accuracy (minimizes the number of errors)? 