Hand Written Text Recognition using Deep Learning
Project Context
Table of Contents
Almost everything in today’s modern world is technologically advanced. Despite the abundance of technological writing tools, many people prefer to take notes the old-fashioned way: with a pen and paper. However, there are certain drawbacks to handwriting text. It is difficult to maintain and access tangible papers, search through them, and share them with others in an efficient manner. As a result, much vital knowledge is lost or never reviewed since documents are never transferred to digital format. This operation requires a large amount of time and effort, which is inefficient for students. Instead of studying more, they spend more time duplicating texts. The researchers have noticed these flaws and seek to assist students by creating an automated platform. “Hand Written Text Recognition using Deep Learning,” the capstone project, is intended to transform hand written text into digital format. This application can distinguish hand written characters in paper. This is quite beneficial to students and other users. If they desire, they can quickly convert any handwritten text to digital format which can be saved timely and is highly shareable. The project will be developed using the Software Development Life Cycle (SDLC) technique by the researchers.
When it comes to tasks like hand-written text recognition, which calls for the capacity to discover and detect patterns in massive amounts of data, deep learning is a subset of machine learning that excels. By creating multi-layered neural networks that can process and interpret data at different levels of abstraction, deep learning algorithms are able to discover these patterns. As a result, the system can distinguish patterns in the general structure of words and sentences as well as patterns in the shapes and strokes of handwritten characters, which is crucial for hand-written text identification.
As it is introduced to additional data, deep learning is also able to learn and get better over time. This is especially helpful for handwritten text recognition because it enables the system to adapt to various writing styles and gradually increase its accuracy.
Objectives of the Study
General Objective – the researchers generally aim to design, develop and implement a Hand Written Text Recognition using Deep Learning.
It specifically aims to:
- Create an application that transforms hand written text from papers into digital format.
- To create a program that can recognize hand written characters accurately.
- To assist students and other end-users to keep digital format of their important notes.
- To have a safekeeping platform for handwritten notes that is easy, accessible and shareable.
- To assess the system’s efficiency, quality, productivity, maintainability, and dependability.
Significance of the Study
The following groups or individuals will benefit from the success of the project:
Students. The success of this application will highly benefit them. This application will come very handy especially if they want to digitally transform their hand written notes and keep it without using physical space. This will help them save more time and effort in transforming notes that they can instead spend on studying.
Researchers. This study will help them further enhance their skills and teamwork in developing successful projects for students.
Future Researchers. They can use the study as basis if they want to develop their version of the Hand Written Text Recognition using Deep Learning.
Possible Features
- There are many possible features and functionalities for a hand-written text recognition system using deep learning. Some examples include:
- Text recognition: The ability to accurately recognize and transcribe hand-written text in a variety of languages and scripts.
- Handwriting recognition: The ability to identify the handwriting of different individuals and to accurately transcribe their handwriting.
- Text classification: The ability to classify hand-written text based on certain characteristics, such as language or genre.
- Handwriting synthesis: The ability to generate new hand-written text that is similar in style to a given sample.
- Handwriting style transfer: The ability to transform the style of a given hand-written text to match a different style.
- Handwriting generation: The ability to generate completely new hand-written text from scratch.
- Handwriting correction: The ability to automatically detect and correct errors in hand-written text.
- Handwriting search: The ability to search for specific hand-written text within a large database of hand-written documents.
Application
There are many potential uses for a hand-written text recognition system in web and mobile applications. One common use is in the field of education, where students can use the system to submit hand-written assignments and have them automatically transcribed for grading. Another use is in the medical field, where doctors and nurses can use the system to quickly transcribe hand-written notes and prescriptions. The system could also be used in a business setting, allowing employees to search for and retrieve hand-written notes and documents. Additionally, the system could be used in a variety of mobile applications, such as a note-taking app or a to-do list app, to allow users to easily search through their hand-written notes and tasks. Overall, the hand-written text recognition system has the potential to greatly improve efficiency and productivity in a variety of settings.
Conclusion
The goal of this research was to evaluate the present method of converting handwritten text to digital format. The developed system was made available to its target users for input. According to the study’s findings, the generated system fits the respondents’ and intended users’ demands and needs. The solution is usable and reliable in assisting students in keeping handwritten tests and making them extremely shareable. As a result, the researchers concluded that the created method is a valuable tool for pupils.
The goal of Hand Written Text Recognition is to convert handwritten text into digital representation. This app can recognize handwritten characters on paper.
Recommendations
The study’s great findings prompted the researchers to vigorously advocate for the system’s implementation. The method is strongly advised because of the efficacy and dependability it may provide to the intended customer. The technique will improve the pupils’ overall experience with transferring handwritten text to digital representation. The researchers point out that in order to correctly use the application, one must first understand how to use the system. Experts advise students to use Deep Learning Hand Written Content Recognition to increase their overall experience and efficiency while converting handwritten text to digital format.
FREE DOWNLOAD SOURCE CODE (template of the project is still on-going, bookmark the page for updates)
Summary
The capstone project, “Hand Written Text Recognition Using Deep Learning,” aims to convert handwritten text into digital representation. This app can recognize handwritten characters on paper. This is really useful for students and other people. If they like, users can swiftly convert any handwritten text to digital format, which can be preserved and shared in a timely manner. The solution is usable and reliable in assisting students in keeping handwritten tests and making them extremely shareable. As a consequence, the researchers concluded that the created approach is a beneficial tool for students. Experts advise students to use Hand Written Text Recognition using Deep Learning to improve their overall experience and efficiency in converting handwritten text to digital format.
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