Optical Character Recognition is the process of converting an input text image into a machine encoded format. Different methods are used in OCR for different languages.

A class diagram for an Optical Character Recognition (OCR) system provides a visual representation of the structure of the OCR application, detailing the various classes involved, their attributes, methods, and relationships. Key classes typically include OCRSystem, ImageProcessor, TextRecognizer, Document, and Character

The following diagram represents the architecture of OCR: So, from the above diagram, we can summarize the steps in the following way: First the user gives an image or other format as the input.

Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from...

segmentation, classification and recognition of digits. Fig 1:- Architecture of the Proposed System The above Figure 1 illustrates the architecture diagram of the proposed system. The proposed model contains the four stages in order to classify and detect the digits: A. Pre-processing B. Segmentation C. Feature Extraction

... diagram of a typical OCR system is depicted in Fig. 1. The main phases of OCR include image acquisition, pre-processing, segmentation, feature extraction, classification, and...

Aug 9, 2017 · It outlines common OCR methods such as grayscaling, binarization, noise removal, sharpening, segmentation, feature extraction, and recognition to identify characters. Diagrams show the system architecture and workflow. Screenshots …

Optical character recognition is the ability of a machine to receive and interpret handwritten input from mul-tiple sources like paper documents, photographs, touch screen devices etc. Recognition of handwritten and machine characters is an emerging area of research and finds extensive applications in banks, offices and industries.

This paper presents a complete Optical Character Recognition (OCR) system for camera captured image embedded /graphics textual documents for handheld devices.

Our project aims to use a mixture of computer vision and handwriting recognition to form a system that acts as a virtual whiteboard. Model users would be ready to write “air words” facing a web camera either real-time or in advance and have those gestures translated into letters or digits.