Optical Character Recognition (OCR) algorithms convert images of text into machine-readable text by following a structured pipeline. Most OCR systems involve three core stages: preprocessing, text detection and segmentation, and character recognition with post-processing. Each stage addresses specific challenges, such as noise in images, varying text layouts, and ambiguous character shapes. By breaking down the process into these steps, OCR systems balance accuracy and efficiency.
The first stage, preprocessing, prepares the image for analysis. This includes converting the image to grayscale to simplify processing, applying filters to reduce noise (like dust or scanner artifacts), and adjusting contrast to separate text from the background. Techniques like binarization (e.g., Otsu’s method) turn the image into black-and-white pixels, making text stand out sharply. Skew correction is also common—for example, aligning a tilted document image by detecting dominant text angles using Hough transforms. These steps standardize the input, ensuring subsequent stages work on clean, normalized data. For instance, a scanned receipt with smudges might undergo morphological operations to fill gaps in characters or remove isolated pixels.
Next, text detection and segmentation identify regions containing text and break them into individual characters. Traditional methods use edge detection (e.g., Canny edges) or contour analysis to locate text blocks. Modern approaches often employ machine learning models like convolutional neural networks (CNNs) to detect text regions, even in complex layouts (e.g., overlapping text in magazine scans). Once text regions are found, segmentation splits lines into words and words into characters. This can involve projection profiling (analyzing horizontal/vertical pixel density to find gaps) or connected-component analysis to group pixels into characters. Challenges arise with cursive scripts or tightly spaced letters—here, algorithms might use dynamic programming or recurrent neural networks (RNNs) to predict segmentation boundaries based on context.
Finally, character recognition and post-processing map segmented characters to their textual equivalents. Classical OCR uses template matching, comparing character shapes to a stored database of glyphs. Modern systems rely on trained models like CNNs or transformer-based architectures. For example, a CNN trained on the EMNIST dataset (a extended MNIST with letters) can classify characters by analyzing pixel patterns. After initial recognition, post-processing refines results using language models or dictionaries. For instance, if the algorithm reads “reciept,” a language model might correct it to “receipt” based on context. Some systems, like Tesseract OCR, use LSTMs to handle sequential text, improving accuracy for sentences by considering adjacent characters. This stage often includes formatting reconstruction, such as preserving paragraph breaks or italicized text detected during segmentation.
By combining these stages, OCR systems handle diverse inputs—from printed documents to handwritten notes—while balancing speed and accuracy. Developers can optimize each step based on use cases; for example, prioritizing segmentation accuracy for historical documents or tuning recognition models for specific fonts. Open-source tools like Tesseract or cloud APIs (e.g., Google Vision OCR) abstract much of this complexity, but understanding the pipeline helps troubleshoot issues like missegmented text or false positives in noisy images.