Image Character Recognition Using Signal & Pattern Analysis

A MATLAB OCR system that recognises text in images. It extracts character patterns from the image, matches them with stored templates and displays the recognised text.

A camera or uploaded image provides the input frame. OpenCV pre-processes it: grayscale conversion, noise removal and resizing. A detector or recognition model finds and identifies the region of interest. The result is displayed, logged in the database and can trigger an alert or action.

Capture the frame, convert to grayscale, remove noise with a Gaussian blur, segment the region of interest with thresholding or edge detection, extract features and finally classify or match them.

First a detector (Haar cascade, HOG or a CNN) finds the face. Then a model converts it into an embedding, a list of numbers that describes the face, and compares it with stored embeddings using a distance measure.

It finds frequent itemsets in transaction data by building larger itemsets only from smaller ones that are already frequent, and then derives association rules with support and confidence above set thresholds.

It follows a three-tier architecture: the browser shows the user interface, the server-side code handles logic and validation, and the MySQL database stores records. Requests travel over HTTP and responses are rendered as HTML or JSON.

All queries use prepared statements with bound parameters, so user input is never joined into the SQL text. Inputs are also validated on the server.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.