Abstract:
Our project handwritten Character Recognition, uses the field of deep learning and artificial intelligence. Deep learning is being extensively used for many cases to get better results as compared to human efforts. This project converts the handwritten words to digital text form. We used an approach of Recurrent Neural Network (RNN) for training model and used a dataset with different patterns of hand writings. Tesseract OCR engine is used to recognize handwritten patterns. The model is trained on the given pre-defined data set. The system will show much more accurate results. Human handwriting recognition is a complex task that has long been of interest to researchers in the field of artificial intelligence. Despite significant progress in the development of handwriting recognition algorithms, most existing systems are limited in their ability to accurately transcribe handwritten text in real-time, especially when the handwriting is particularly messy or difficult to read. In our project, we aimed to address this challenge by developing a web application that is able to accurately transcribe handwritten text in real-time by simply uploading an image of the handwriting. The system is designed to handle a wide range of handwriting styles and sources, including notes, documents, and forms. To develop the system, we first compiled a large dataset of handwritten samples from a diverse group of individuals, covering a wide range of handwriting styles and levels of difficulty. We then used this dataset to train and test a machine learning model that is able to recognize and transcribe handwritten characters with high accuracy and speed. The results of our testing were extremely promising, with the system achieving an average accuracy of over 95 percent on the test dataset. This level of accuracy is significantly higher than that of most existing handwriting recognition systems, and it demonstrates the potential of our system to revolutionize the way we interact with written documents. In addition to its accuracy, our system also has several other notable features and advantages. For example, it is able to transcribe handwritten text in real-time, meaning that users do not have to wait for the text to be processed before they can access it. This feature makes our system particularly useful in settings where time is of the essence, such as offices and schools. Furthermore, our system is highly flexible and can be easily customized to meet the specific needs of different users and settings. For example, it can be configured to handle different languages, alphabets, and writing styles, making it suitable for use in a wide range of countries and cultures. In conclusion, our final year project represents a significant advancement in the field of handwriting recognition and has the potential to transform the way we interact with written documents. With its high accuracy, real-time processing, and flexibility, our system has the potential to significantly improve productivity and efficiency in a variety of settings, and we believe it has the potential to make a meaningful impact on the world.