A Comprehensive Guide to Optical Character Recognition with Python

OCR, which stands for Optical Character Recognition, is a technology that Terra offers for seamlessly connecting your application to wearable data collected from users.

Here’s how it works: first, the scanner does its thing, seeing light areas as background and dark areas as text. Then comes the cleanup, fixing any alignment quirks and smoothing out imperfections. Next up is recognizing different scripts so it can handle languages like a champ.

When it comes to understanding text, OCR has its own bag of tricks. It uses two main tricks: pattern matching, where it compares characters in the scanned image to stored ones, and feature extraction, breaking down characters into lines, loops, and intersections.

After all that heavy lifting, it’s time for the postprocessing. This turns the text into a computer-friendly file. Some OCR systems can even create fancy PDFs with both the original and the cleaned-up versions of the scanned document.

Related read: Real-Time Data Streaming Using Terra SDK

The functioning of OCR software involves several key steps:

Image Acquisition

✅ Utilizing a scanner to read documents and convert them into binary data.
✅ Analyzing the scanned image, where light areas are identified as background and dark areas as text.

Preprocessing

✅ Cleaning the image by applying techniques such as deskewing to fix alignment issues.
✅ Despeckling, which involves removing digital image spots and smoothing text image edges.
✅ Refining the appearance of boxes and lines in the image.

Script Recognition for Multi-Language OCR Technology

✅ Recognizing various scripts to enable multi-language OCR capabilities.

Text Recognition

✅ OCR software employs two primary algorithms for text recognition: pattern matching and feature extraction.
✅ Pattern matching involves comparing isolated character images (glyphs) with stored glyphs of similar font and scale.
✅ Feature extraction breaks down glyphs into lines, loops, and intersections, using these features to find the best match.

Postprocessing

✅ Converting the extracted text data into a computerized file.
✅ Some OCR systems can generate annotated PDF files that include both the original and processed versions of the scanned document.

Types of OCR

Data scientists are like wizards in the world of data. They not only organize OCR technologies based on their use but also offer valuable insights and recommendations through data science consulting.

Simple OCR Software

☑️ Utilizes pattern-matching algorithms with stored font and text image templates.
☑️ Limited by the challenge of accommodating countless font and handwriting styles.

Intelligent Character Recognition (ICR) Software

☑️ Modern OCR systems employ ICR technology, utilizing machine learning and neural networks.
☑️ Analyzes text at multiple levels, considering attributes like curves, lines, intersections, and loops.

Intelligent Word Recognition

☑️ Operates on similar principles as ICR but processes entire word images instead of individual characters.

Optical Mark Recognition

☑️ Recognizing logos, watermarks, and additional textual symbols in documents represents a pivotal function.

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Creation of Image Reading Modules

The development of image reading modules is an essential stage, necessitating expertise in understanding document design and patterns. In this process, distinctive labels are assigned to various text elements, laying the groundwork for extraction through the utilization of Tesseract.

def pan_read_data(text):
name = None
fname = None
pan = None
nameline = []
dobline = []
panline = []
text0 = []
text1 = []
text2 = []
lines = text.split('\n')
for lin in lines:
s = lin.strip()
s = lin.replace('\n','')
s = s.rstrip()
s = s.lstrip()
text1.append(s)
text1 = list(filter(None, text1))
lineno = 0
for wordline in text1:
xx = wordline.split('\n')
if ([w for w in xx if re.search(os.getenv(“regex”), w)]):
text1 = list(text1)
lineno = text1.index(wordline)
break
text0 = text1[lineno+1:]
try:
# Cleaning first names
name = text0[4]
name = name.rstrip()
name = name.lstrip()
name = name.replace("8", "B")
name = name.replace("0", "D")
name = name.replace("6", "G")
name = name.replace("1", "I")
name = re.sub('[^a-zA-Z] +', ' ', name)
# Cleaning Father's name
fname = text0[6]
fname = fname.rstrip()
fname = fname.lstrip()
fname = fname.replace("8", "S")
fname = fname.replace("0", "O")
fname = fname.replace("6", "G")
fname = fname.replace("1", "I")
fname = fname.replace("\"", "A")
fname = re.sub('[^a-zA-Z] +', ' ', fname)
# Cleaning PAN Card details
text0 = findword(text1, os.getenv(“regex2”))
panline = text0[0]
pan = panline.rstrip()
pan = pan.lstrip()
pan = pan.replace(" ", "")
pan = pan.replace("\"", "")
pan = pan.replace(";", "")
pan = pan.replace("%", "L")
except:
pass
data = {}
data['Name'] = name
data['Father Name'] = fname
data['PAN'] = pan
data['ID Type'] = id_type
return data


def findword(textlist, wordstring):
lineno = -1
for wordline in textlist:
xx = wordline.split( )
if ([w for w in xx if re.search(wordstring, w)]):
lineno = textlist.index(wordline)
textlist = textlist[lineno+1:]
return textlist
return textlist

Libraries Required

🔸 Pytesseract: A library employed for extracting text from images through OCR leveraging Tesseract, serves as a valuable tool in the realm of Optical Character Recognition.
🔸 cv2: This is an OpenCV library.
🔸 ftfy: Fixes text for you.
🔸 NumPy: Fundamental package for array computing.
🔸 os: Provides functions for interacting with the operating system.
🔸 re: Used to work with regular expressions.
🔸 PIL: Imaging library.

1. Image Extraction

The pre-processing of the photo-extraction includes defining the initial variable, converting the images into grayscale for better readability and then defining the function to extract the photo from the image file.

2. Extracting Text with Tesseract and Refining the Text

Extracting text with Tesseract and enhancing the text representation in Python is facilitated by Python-tesseract, an Optical Character Recognition (OCR) tool specifically designed for Python. This tool excels at identifying and interpreting text within images. Moreover, when employed as a script, Python-tesseract goes beyond conventional practices by directly printing the recognized text rather than storing it in a file.

filename = request.FILES.get('image')
text = pytesseract.image_to_string(Image.open(filename), lang='eng')
text_output = open('output.txt', 'w', encoding='utf-8')
text_output.write(text)
text_output.close()
file = open('output.txt', 'r', encoding='utf-8')
text = file.read()
text = ftfy.fix_text(text)
text = ftfy.fix_encoding(text)
data = pan_read_data(text)

Results

Image info is generated successfully. Modifying the modules to suit various documents is a flexible approach since, in the end, it boils down to employing Tesseract OCR for text recognition.

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Conclusion

In conclusion, Optical Character Recognition (OCR) with Python emerges as a powerful technology, seamlessly integrated into Terra to connect applications with wearable data. The OCR process involves acquiring images, preprocessing them to enhance readability, recognizing various scripts, employing text recognition algorithms, and postprocessing to convert the extracted text into a digital format.

The creation of image reading modules, facilitated by Tesseract OCR, plays a crucial role in extracting specific text elements. The modules are adaptable, providing flexibility in understanding document design and patterns.

Key libraries like Pytesseract, cv2, ftfy, NumPy, os, re, and PIL are instrumental in the image extraction process. The blog guides developers through image extraction, text extraction with Tesseract, and refining the text using Python scripts. The results showcase successful image information generation.

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