International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Handwritten Text Recognition Using Machine Learning Erram Aishwarya Reddy1, Kasireddy Raghuvardhan Reddy2, Mohammad Irshad3 1Student & Sreenidhi Institute of Science and Technology, Ghatkesar
2Student & Sreenidhi Institute of Science and Technology, Ghatkesar 3Professor, Dept. of Computer Science and Engineering, Sreenidhi Institute of Scinence and Technology, Telangana,
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Abstract - The objective of HTR is to automate the process
Using both CNN’s and RNN’s: There are few HTR systems which are built using combination of the CNN's and RNN's where the data is loaded into the training model is first passed into the set of CNN layers and then the outcome of the CNN layers is passed through the RNN layers to train the model and prepare the model.
of converting handwritten documents into digital text, which is much easier to store, edit, and search. HTR is used in various applications, including digitizing historical documents, recognizing handwriting in online forms, and improving accessibility for people with visual impairments. We propose a system that uses both the CNN and RNN neural networking algorithms to predict the Handwritten text recognition.
2.2. Proposed System: Proposed system contains a set of CNN layers which would take the inputs from the dataset that is given to train the model and that would load the data into 7 layers of CNN (Continuous Neural Networks) and give the output to the set of RNNs (Recurrent Neural Networks) then the output of both CNNs and RNNs are given to CTC a model of Tensor flow.
Key Words: CNNs, HTR, RNNs, CER, Accuracy, Recognition, Training
1. INTRODUCTION Handwritten text recognition (HTR) is an area of artificial intelligence that deals with the development of algorithms capable of recognizing and interpreting handwritten text. HTR aims to automate the process of turning handwritten papers into editable, searchable, and readily stored digital text. HTR can be used for a variety of things, such as digitizing old documents, reading handwriting on the screen, and enhancing accessibility for those with visual impairments. Preprocessing, feature extraction, and classification are some of the processes that make up a typical HTR system. The input image is improved upon and prepared for future processing during the preprocessing stage. The neural network extracts feature from the preprocessed image during the feature extraction stage that are important for reading the handwritten text.
2.3 Proposed system Architecture: The architecture depicted in the diagram is a deep learning model used for text recognition. The model takes in a batch of images where each image has dimensions of (batchSize, imgSize[0], imgSize[1]), where imgSize is a tuple that specifies the height and width of the image, and batchSize is the number of images fed into the model at once.
2. LITERATURE SURVEY 2.1 Existing System: Using CNN’s: Handwritten Text Recognition (HT) using Convolutional Neural Networks (CNNs) has become increasingly popular in recent years. The primary benefit of CNNs is their capacity to automatically extract pertinent characteristics from the input picture, which makes them especially well-suited for HTR and other image identification tasks.
Fig -1: Design of the Model
3. UML DIAGRAMS
Using RNN’s: There are various HTR systems in use today that exclusively use RNNs to recognize handwritten text. The Long Short-Term Memory (LSTM) network, a kind of RNN that is intended to better capture long-term dependencies in the data, is one well-known example.
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3.1 Use case Diagram In the Unified Modelling Language (UML), a use case diagram is a particular kind of behavioral diagram that shows how a system interacts with users or other entities. This diagram
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