The conversation of image to text is no longer an arduous task, thanks to the advancements in Optical Character Recognition (OCR) technology. It’s ingenious at simplifying complex jobs, such as transcribing handwritten content to text, querying image searches via text, and cloning documents sans typing. Sure, it might seem akin to sorcery, but keep reading for a detailed walkthrough of its mechanism.
Decoding the Core Functionalities oif OCR
To harness the magic of OCR, it’s beneficial to first comprehend how an image is stored in computers. The crux lies in pixels—the minuscule, distinct dots that constitute an image. The more the pixels, the crisper the image. However, to a computer, all files—textual or not—are an assortment of distinct colored pixels. Recognizing text from this pixelated view is the essence of OCR. Below is a step-by-step guide to unravel how it achieves this.
1. The Pre-Processing Stage
In this phase, the image is meticulously prepared for text extraction. Different software employ diverse pre-processing techniques to ensure minimal errors. Widely used pre-processing techniques include:
- Binarization: In this technique, the image’s pixels are made black or white, delineating text and background distinctly for faster OCR.
- Deskew: This technique aligns tilted or inverted characters caused by uneven scanning, making the text more readable.
- Despeckling: It smoothens the image by eradicating noise which could obstruct text recognition.
- Removing Lines: All irrelevant lines in an image are eliminated to avoid confusion during OCR process.
- Zoning: This unique technique separates individual columns in an image, preventing text overlapping.
2. Image Processing
Now on to the processing phase where a baseline is established for each image text line. Any overlooked pixels during pre-processing are detected in this phase. OCR software identifies character gaps using vertical lines and non-text pixels. This is known as tokenization. Subsequently, OCR software features two distinct strategies to identify characters:
- Matrix Matching: Each token is compared with the software’s set of recognized characters like alphabets, numbers, symbols, etc. The closest match is selected by OCR.
- Feature Extraction: Here, each token is compared based on predefined rules signifying certain character types. This process is complex yet adept at differentiating minor variations among similar entities like capital I, lowercase L, and digit 1.
3. Post-Processing
This final phase further enhances the results before revealing them to the user. Some key steps in this phase include:
- Lexical Resource Limitation: Extracted words are compared to a finite collection, replacing unidentifiable words with closest matches.
- Application-Specific Optimizations: Specialized OCR is employed for specific context use-cases like legal or medical documents.
- Natural Language: This language-centric step arranges words in proper sentences to correct any linguistic or grammatical errors.
The intricacies of OCR might appear intricate, but remember that each tool can yield varying results, depending on the techniques employed. One such tool that I reckon for its efficiency and user-friendly interface is the ‘JPG to Text Converter’. With this online tool, your conversions will be accurate, and it facilitates uploading up to 5 images simultaneously with a simple drag-and-drop feature.
