Hand Written Pattern Recognition System detects and interpret handwriting from documents or images. It is a technique used in many areas, such as automatic document processing, handwriting recognition, document analysis, and computer vision. It uses image processing techniques to identify and interpret characters, such as letters, numbers, and symbols, that are written in natural handwriting. The system is typically composed of four components: preprocessing, feature extraction, pattern matching, and classification. Preprocessing involves scanning the document or image, cleaning it up, and converting it into a digital format. Feature extraction is the process of extracting relevant features from the image. Pattern matching is the process of comparing the extracted features with a set of known patterns. Finally, classification is the process of assigning the extracted features to the most likely pattern.

A handwritten pattern recognition system is a computer system that is designed to recognize patterns in handwritten input. This can be useful for a variety of applications, such as handwriting recognition for text entry, signature verification, and identifying handwritten notes or documents.

Software Requirements of Hand Written Pattern Recognition System:

• Java 1.4 or More
• Swings
• Windows 98

 Hardware Requirements:

• Processor                     :           Pentium IV 2.6 GHz
• RAM                           :           512 MB
• Monitor                       :           15”
• Hard Disk                   :           20 GB
• CD Drive                    :           52X
• Keyboard                    :           Standard 102 Keys

There are several key components to a handwritten pattern recognition system:

1. Preprocessing: This involves preparing the handwritten input for analysis by the system. This might involve image processing techniques such as filtering, binarization, and segmentation to enhance the quality of the input and make it easier for the system to recognize patterns.
2. Feature extraction: This involves identifying and extracting relevant features from the preprocessed input. These features might include stroke width, curvature, and connectivity between strokes.
3. Classification: This involves using machine learning algorithms to analyze the extracted features and assign them to predefined classes or categories. This might involve training a classifier on a labeled dataset of handwritten examples.
4. Evaluation: This involves assessing the performance of the system by comparing its output to a known set of correct answers. This can help identify any weaknesses or errors in the system and allow for improvements to be made.

Overall, a handwritten pattern recognition system can be a useful tool for automating the process of recognizing and interpreting handwritten input.