Printed or handwritten text is often a stumbling block for efficient work related to research and the collection of data from documents. With the right technology, the entire research process can be fast-tracked. Optical Character Recognition (OCR) technology is capable of converting 2-dimensional images of handwritten or printed text into machine-readable text.
OCR technology follows the same operational process in all its applications.
- Preprocessing of the Image: The text, handwritten, or printed is first converted into a two-dimensional image through an imaging tool such as a camera or scanner. This image has to be enhanced in order to facilitate the accurate identification of characters. In this stage, the captured image is modified in order to optimize its quality.
- Localization of text: The OCR toolkit then analyses the layout of the enhanced image. AI-based algorithms and ABBYY Adaptive Document Recognition Technology are used by the OCR toolkit for this purpose. The goal of this step is to localize or recognize the region of the image containing the written or printed characters
- Character Identification: The fine reader Engine within the toolkit identifies the characters as alphabets, numbers, and special characters.
- Character Segmentation: Identified characters are then classified into segments according to their nature.
- Post-processing: Once the characters are machine-readable and segmented, they can be exported into multiple file formats or made to undergo processing steps such as editing or translation.
Each process has a set of sub-processes, these sub-processes may vary between applications, but their basic principles remain the same.
Pytesseract or Python-tesseract is an OCR toolkit built for Python. Python-tesseract acts as a wrapper for Google’s Tesseract-OCR Engine. However, it functions well as a stand-alone invocation script to tesseract. This is because of its capabilities to read all formats of image supported by the Leptonica and Pillow imaging libraries. These include jpeg, png, gif, bmp, tiff, and among several other formats.
Apart from these functions, Python-tesseract is capable of printing the recognized text instead of writing it to a file when it is used as a script.
In order to convert an image to a string, Pytesseract has to be downloaded and installed on the users’ device. Once the installer has been downloaded, Psytesseract can be installed by running the following command on the users’ terminal.
pip install psytesseract
Once installed, the user can extract text from images. Before extracting texts from an image, the image has to be preprocessed and enhanced, so that the characters are in black on a white background. Also, the OCR engine does not function properly with images that have a lot of noise or distortion and the typography or font of the text must be common.
The following step is to import the image class from the Pillow image library. This is required in order to load the input image from the users’ storage device in the PIL format specified by the Pillow Library. The image is then imported into Pytesseract. In the path, the tesseract executable has to be included.
The next step is to create an image object of the PIL Library and pass the image into the Pytesseract module. The image_to_string command returns the result of a Tesseract OCR run on the image to string.
The resulting text appears in the output of the terminal which can then be printed out.
KlearStack specializes in AI-driven data extraction for the automation of payment-related processes. The KlearStack software, which features quick set-up times and negligible setup costs, transcends beyond normal OCR tools with its template-independent extraction feature. This saves the endless hours spent on custom template designing.
The KlearStack AI uses machine learning with adaptive deep learning models to easily identify useful and valid information from financial documents even if they are unstructured or if their layouts vary. There is a lot of valuable information in unstructured data, KlearStack AI is able to extract insights from it and use it to automate processes related to payment, thereby saving the time and money spent on manual data analysis from unstructured documents.
