Top 11 PYTHON OCR LIBRARIES

The top Python OCR libraries can extract text from images and perform searching and other analysis operations.  

The procedure used to transform an image of text into a machine-readable text format is known as optical character recognition (OCR). It is a commercial system for automating data extraction from printed or written text from scanned documents or picture files, then turning the text into a machine-readable form for data processing like editing or searching. For instance, if you scan a form or a receipt, your computer stores the scan as an image file. The information can then be used to automate processes, streamline operations, and increase productivity.  

OCR libraries developed using python are listed below. These are optimized so that the process of OCR is simplified. 

PaddleOCR- 

• Multilingual OCR tools to train better models. 
• Layout analysis and Table Recognition optimization. 
• A visual independent model for key information extraction. 

EasyOCR- 

• Supports 80+ languages and is ready to use.  
• Scripts of all popular languages, including Chinese, Arabic, etc.  
• The output will be presented as a list, with each item denoting a bounding box, the amount of text detected, and the confidence level. 

OCRmyPDF- 

• Makes a pdf searchable by adding an OCR layer. 
• The exact resolution of the original image is maintained. 
• Highly scalable and can handle pdfs with multiple pages. 
• Can also validate input and output files. 

ocropy- 

• Can be used for document analysis alongside OCR. 
• The text-line recognizer is robust, while the layout analysis is resolution dependent. 
• Image pre-processing and training models are required. 

ExtractTable-py- 

• Specifically for extracting tabular data from images or pdf.  
• Table area, column coordinates, and other specifications are taken care of.  
• It is an API authorized using an API key. 

LiPlate- 

• OpenCV script that takes images of cars as input. 
• Reads the license plate number extracted from the image. 
• The Tesseract library is needed for the Tesseract-OCR version.

ocr- 

• Uses neural networks for Optical Character Recognition.  
• Implemented using NumPy and OpenCV. 
• Noises can be removed and segmented for better OCR. 

keras-ocr- 

• High-level API for text detection and OCR pipeline. 
• Inspired by CRAFT text detection model. 
• Punctuation and letter case is ignored. 

pytesseract- 

• Python version of Google’s Tesseract. 
• Stand-alone invocation script to Tesseract. 
• The recognized text can be printed instead of written into a file.

calamari- 

• ATR engine-based Optical character recognition.  
• Operates on the text-line level, and line segmentation is required.  
• Modular, customizable, and command line interface. 

LaTeX-OCR- 

• Extract an image of a formula and convert it into latex code. 
• Already existing images, as well as images in the clipboard, can be analyzed. 
• Efficient and user-friendly interface for better model prediction.