Handwritten Text Recognition Using Machine Learning

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 880
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,
India
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Abstract - The objective of HTR is to automate the process
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applications, including digitizing historical documents,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. Weproposea
system that uses both the CNN and RNN neural networking
algorithms to predict the Handwritten text recognition.
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, readinghandwritingonthescreen,
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. 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 HTRandotherimageidentification
tasks.
Using RNN’s: There are various HTR systems in use today
that exclusively use RNNs to recognizehandwrittentext.The
Long Short-Term Memory (LSTM) network, a kind of RNN
that is intended to better capture long-termdependencies in
the data, is one well-known example.
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.
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 theoutputtotheset
of RNNs (Recurrent Neural Networks) then the output of
both CNNs and RNNs are given to CTC a model of Tensor
flow.
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.
Fig -1: Design of the Model
3. UML DIAGRAMS
3.1 Use case Diagram
In the Unified ModellingLanguage(UML),a usecasediagram
is a particular kind of behavioral diagram that shows how a
system interacts with users or other entities. This diagram

provides a simple-to-understand representation of a
system's functioningandpotential applications.Itismade up
of actors, use cases, and the connections among them. Use
cases define how the system behavesfromtheperspectiveof
the user, whereas actors represent the various user types or
outside entities that interact with the system. Use cases and
actors may be linked together by straightforward
associations or more intricate linkages like generalizations,
extends, and includes.
Fig -2: Use Case Diagram
3.2 Activity Diagram
A sort of behavioral diagram in Unified Modelling Language
(UML) called an activity diagram shows how activities or
actions move through a system. It offers a visual depiction of
the procedures or actions necessary to finisha particularjob
or use case within the system. The nodes in the activity
diagram stand in for the various activities or actions, while
the edges show the order in which those activities oractions
are carried out. The nodes might represent straightforward
processes, like sending a messageormakinga choice,orthey
can be more intricate processes with several phases. The
boundaries might be solid lines to denote a predetermined
order of events or dotted lines to denote other routes or
conditional branching.
Fig -3: Activity Diagram
3.3 Class Diagram
A class diagram is a type of UML diagram that is used to
represent the structure of a system by illustratingitsclasses,
attributes, methods, and the relationships between them. It
is a graphical representation that enables developers to
visualize the different components of a systemandhowthey
interact with one another. The primary purpose of a class
diagram is to describe the classes in a system and their
relationships with each other. The class diagramcanbeused
during various stages of software development, from design
to implementation and maintenance.
Fig -4: Class Diagram

4. REQUIREMENTS
4.1 Hardware Requirements
 Processor: Intel Core i5 and above
 RAM: 8GB and above
 Memory: 512GB SSD and above
4.2 Software Requirements
 Windows or macOS
 Visual Studio
 Python 3.6
5. METHODS USED
5.1. Data Collection:
Data collecting is a crucial component of machine learning,
and the caliber of the data has a direct bearing on the
precision of the models that are produced. The practice of
acquiring information about user access to and activity
inside the systems and applications of an organization is
referred to as data collection in the context of identity and
access management (IAM). Timestamps for login and logout
attempts, unsuccessful login attempts, access requests,
permissions, and other information can be included in this
data. In general, gathering data is a crucial step in utilizing
machine learning to improve IAM capabilities inside an
organization. The accuracy and use of the data obtained may
be ensured by careful planning, execution, and
preprocessing, resulting in more efficient machine learning
models and a better security posture.
5.2. Data Preprocessing:
A crucial step in every machine learning project is data
preprocessing, which involves a number of methods and
procedures used to convert raw data into a format that can
be utilized for training a model. This step comprises
extensively cleaning and pre-processing the obtained data,
which entails locating and fixing problemsincludingmissing
data, outliers, inaccurate values, and duplicate
characteristics. The next step is to change the data's format
so that it is acceptable for analysis. This may require
normalizing, standardizing, scaling features, and encoding
categorical data.Byminimizingmistakesandinconsistencies
in the data, this stage helps the model function more
accurately and produce findings that can be trusted.Overall,
data preparation is a crucial part of machine learning, and
the precision and success of the final model may be greatly
influenced by the caliber of the input data and the efficiency
of the preprocessing methods used.
5.3. Training the model:
It is also essential to select the right algorithm for the job at
hand. Machine learning algorithms come in many different
varieties, such as supervised learning, unsupervised
learning, and reinforcement learning. When a labelled
dataset is available, supervised learning techniques are
applied; the system is trained ontheinputandoutputdatato
predict future output. When there are no labels on the data
and the computer must figure out patterns and connections
on its own, unsupervised learning approaches are utilized.
Models are taught to make decisions based on input from
their surroundings through reinforcement learning. The
efficacy and precision of the trained model are evaluated
using a test dataset. The model may also be applied to fresh
data if it successfully predicts the test data.In conclusion,
there are several steps involved in using machine learning
algorithms to train a model. Among these processesaredata
preparation, picking the best strategy, and model
optimization. These methods may be used to create precise
and potent machine learning models for a range of
applications.
5.4. Recognition:
The model may occasionallyneedto betweakedorretrained
with new data to improve performance. The approach may
be applied in different circumstances to identify trends or
make predictions in real-time applications. A trained image
recognition model may be used to identify objects in images
or videos, in contrast to a taught language model, which can
be used to generate text or make language-based
predictions. Overall, the ability to analyze data quickly and
accurately can lead to significant improvementsinefficiency
and decision-making, which is why the use of trained
machine learning models for prediction and recognition
tasks has a wide range of real-world applications in sectors
like manufacturing, healthcare, and finance.
5.5. Output Generation:
A few critical components arerequiredforHandwritten Text
Recognition output creation on an intuitive interface to
guarantee an effective and user-friendly process. The
recognition system's accuracyshouldbeconsideredinitially.
A highly precise system will generate output that is more
dependable, which will boost userhappinessandconfidence.
Users want the result to be created promptly and in real-
time, therefore speed of the system is equally important to
accuracy. The user interface itself is another crucial factor
for output production. The user should be provided with
clear instructions and feedback via an intuitive and simple-
to-use interface. The result must be presented clearly and
succinctly, ideally with formatting choices so that the user
may adjust it to their own requirements. A few critical
components are required for Handwritten Text Recognition

Fig -5: Accuracy
Fig -6: Output 1
Fig -7: Output 2
6. APPLICATION
 Document Digitization
 Postal Services
 Education
 Banking
 Historical Archives
 Medical Services
 Law Enforcement
7. CONCLUSION
Handwritten Text Recognition is an importantapplicationof
machine learning that has the potential to significantly
improve productivity and efficiency in various industries.
The use of Convolutional Neural Networks and Recurrent
Neural Networks has proved to be an effective approach to
achieve high accuracy in recognizinghandwrittentext.CNNs
are used for feature extraction from images of text, while
RNNs are used to model the sequential nature of
handwriting. Now that several neural network designs have
been merged, it is feasible to develop sophisticated models
that have a high degree of handwritingrecognitionaccuracy.
However, the quality of the training data and the tuning of
the hyperparameters have a significant impact on how well
the Handwritten Text Recognition systemperforms,justlike
with any machine learning model. It is essential to
output creation on an intuitive interface to guarantee an
effective and user-friendlyprocess.Therecognitionsystem's
accuracy should be considered initially. A highly precise
system will generate output that is more dependable, which
will boost user happiness and confidence. Users want the
result to be created promptly and in real-time, therefore
speed of the system is equally important to accuracy. The
user interface itself is another crucial factor for output
production. The user should be provided with clear
instructions and feedback via an intuitive and simple-to-use
interface. The result must be presented clearly and
succinctly, ideally with formatting choices so that the user
may adjust it to their own requirements.
thoroughly test and assess the model to ensure that it
performs well with a variety of handwriting inputs and
styles. Planning and executing tests for handwritten text
recognition should consider the unique qualities and
challenges of this application as well as the need to test for
accuracy, robustness, and scalability.
8. FUTURE SCOPE
With the use of CNNs and RNNs, handwritten text
recognition has advanced significantly in recent years and
may continue to do so. The accuracy and effectiveness of
handwriting recognition models may be increased in a few
ways with the development of deep learning techniques.
Convolutional and recurrent neural network hybridmodels,
which can capture both spatial andsequential informationin
the input, are one field of research. Enhancing the models'
capacity to recognize handwriting patterns from many
languages and cultures is another area of emphasis, since
doing so may open the door to a more all-encompassing and
universal method of text identification.
REFERENCES
[1] "Handwritten Text Recognition: An Introduction" by
Stefan Jaeger, Stephan Rusinol and Ernest Valveny.
[2] "Handwriting Recognition: Soft Computing and
Probabilistic Approaches" by Sargur N. Srihari.
[3] "Handwriting Recognition: A Comprehensive Guide" by
Sargur N. Srihari.
[4] "Deep Learning for Computer Vision" by Adrian
Rosebrock.
[5] "Python Machine Learning" by Sebastian Raschka.
[6] "TensorFlow for Deep Learning" byBharathRamsundar
and Reza Bosagh Zadeh.

[7] "PyTorch Deep Learning Hands-On" by Sherin Thomas
and Sudhanshu Passi.
[8] The TensorFlow website
(https://www.tensorflow.org/) and PyTorch website
(https://pytorch.org/) offer a wealth of tutorials and
documentation for deep learning,includinghandwriting
recognition using CNNs and RNNs.

Handwritten Text Recognition Using Machine Learning

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