![]() LSTM based models excel at complex tasks with a firm definition that can be learned through a training set. import cv2 import sys import pytesseract if name = ‘main’: if len(sys.argv) < 2: print(‘Usage: python ocr_receipt.py receipt.jpg’) sys.exit(1) # Read image path from command line imPath = sys.argv # Uncomment and complete the line below to provide path to tesseract # _cmd = ‘/usr/bin/tesseract’ # Parameters: ‘-l eng’ for using the English language LSTM OCR Engine config = (‘-l eng - oem 1 - psm 3’) # Read image from disk im = cv2.imread(imPath, cv2.IMREAD_COLOR) # Run tesseract OCR on image text = pytesseract.image_to_string(im, config=config) # Write recognized text to file f = open(‘receipt_text.txt’, ‘w’) f.write(text) f.close() Conclusion Output is to a file within local directory. For this project, pytesseract is pretrained to find only characters or numeric from the English language and will exclude information that is not a letter or number within that defined set. Using little code, the image can be converted to text using a process of layers of learning to understand text from images and return only characters using layers of repetition to “drop out” leaving only text. ![]() Next, open Python with the pytesseract and cv2 libraries installed. ![]() Project, Image to Textįor this example, take a picture of a receipt and save to local directory. This is known as text extraction from an image. This approach is deep learning using recurrent neural network (RNN), Long Short Term Memory (LSTM), to take an image as input and output text from the image in a file. Then, these pieces placed together to output a result without error that is same as the original object. This is a complicated task that requires an image to be statistically evaluated and assigned the highest probably match for each portion for a recognizable letter. TEXT EXTRACTOR PYTHON SOFTWAREWhy AI?Ĭreating software to translate an image into text is sophisticated but easier with updates to libraries in common tools such as pytesseract in Python. OCR addresses this, and a piece of OCR is knowledge from images. ![]() Creating a definition of a picture, understanding content, is a complex task. Taking that further, there is Optical Character Recognition (OCR) that can take a picture of text and create a usable file that is same as document. When taking the picture, there is recognition of that picture and often an autocorrection. Many devices include cameras for taking pictures. Image capture makes a snapshot in time of a person, place, or object. ![]()
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