Mini Project Seminar
On
Digitalization of Handwritten Text
using Neural Networks
Gayatri Vidya Parishad College of Engineering
(Autonomous)
Madhurawada,Visakhapatnam-530 048
Under the esteemed guidance of
Dr.Ch.Sita Kumari
Sr. Assistant Professor
Department of Information Technology
Project Team Members
K.Anusha 17131A1251
A.Sailaja 17131A1206
Ch.Anjani Nookambica 17131A1223
N.Amulya 17131A1207
ABSTRACT
Handwritten Text Recognition has been one of the active and challenging research areas
in the field of image processing and pattern recognition. It has numerous applications
which include reading aid for partially blind, bank cheques and conversion of any
handwritten document into structural text. In this project, an attempt is made to recognise
handwritten characters for English alphabets . We use a NN for our task. It consists of
convolutional NN (CNN) layers, recurrent NN (RNN) layers and a final Connectionist
Temporal Classification (CTC) layer. The dataset contains text and digits. In this project,
each text image is resized into 128X32 pixels which is directly subjected to Training. That
is, each resized image has 4096 pixels and these pixels are given as input to the neural
network. The trained network is used for classification and recognition.
INTRODUCTION:
The objective of this project is to take handwritten English text or digit
image as input,process the text, train the neural network algorithm,to
recognize the text.
This project is aimed at developing software which will be helpful in
recognizing characters of English language and digits.This project is
restricted to English characters only.It can be further developed to
recognize the characters of different languages.It engulfs the concept
of neural network
PURPOSE:
• Document Reading
• Conversion of any handwritten document into structural text form.
Input layer Hidden layers Output layer
79 neurons
neurons
4096
Neural network
Architecture
TECHNOLOGIES USED:
• CNN(Convolutional Neural Networks)
• RNN(Recurrent Neural Networks)
PLATFORMS USED:
• Pycharm
• Python 3.6
EXISTING SYSTEM:
• Optical Character Recognition(OCR) is the existing system
for character recognition.
• It is an electronic translation of images of hand-written ,
type-written or printed text into machine editable text.
Drawbacks:
• It doesn’t have noise reduction.
• Direct use of OCR remains difficult problem to resolve,as it
leads to low reading accuracy.
PROPOSED SYSTEM:
• We use a NN for our task. It consists of convolutional NN (CNN)
layers, recurrent NN (RNN) layers and a final Connectionist
Temporal Classification (CTC) layer.
• The input image is fed into the CNN layers. These layers are
trained to extract relevant features from the image.
• The RNN output sequence is mapped to a matrix of size 32×80.
• While training the NN, the CTC is given the RNN output matrix and
the ground truth text and it computes the loss value.
REQUIREMENTS SPECIFICATION:
HARDWARE REQUIREMENTS:
• Ram:4GB or higher
• Disc Space:1TB
• Processor:Intel i5 or higher
SOFTWARE REQUIREMENTS:
• Operating System:WINDOWS
• Python3
• Packages :TensorFlow,numpy,opencv,keras
• Pycharm
FUNCTIONAL REQUIREMENTS:
• The system should process the input given by the user only if it is
an image file.
• System will show the error message to the user when the input
given is not in the required format.
• System should detect the characters present in the image.
• System should retrieve characters present in the image and
display them to the user.
NON-FUNCTIONAL REQUIREMENTS:
• Performance: Handwritten characters in the input image will
be recognized with high accuracy.
• Functionality: This software will deliver on the functional
requirements mentioned in this document.
• Availability: This system will retrieve the handwritten character
regions only if the image contains written characters in it.
• Recognition Ability: The software is very easy to use and
recognizes the characters from the image.
• Reliability: This software will work reliably for any type of
character images.
SYSTEM ARCHITECTURE/FLOW CHART:
Start
Real
Image
Noise
Removal
Classification of
image
Extraction of text from image
Text contained in the image
will be displayed
Stop
Image Acquisition
Preprocessing
Segmentation
Classification and
Recognition
Post processing
Process Flow
Image Acquisition:
• In Image acquisition,the recognition system acquires a scanned
image as an input image.
• The image should be in png format.
Pre-processing:
• The pre-processing is a series of operations performed on
scanned input image .
• Generally, noise filtering , smoothing should be done in this step.
• The pre-processing also defines a compact representation of the
character .
• Binarization process converts a gray scale image into a binary
image.
• Dilation of edges in the binarized image is done.
Segmentation:
• In this stage, an image of sequence of characters is decomposed into
sub-images of individual characters.
• The pre-processed input device is segmented into isolated characters by
assigning a number to each character using labelling process.
• Labelling process provides information about number of characters in
image.
• Each individual character is uniformly resized into pixels.
Classification And Recognition:
• The classification stage is the decision making part of the recognition
system.
• A feed forward back propagation neural network is used in this work
for classifying and recognizing the handwritten characters .
• The total number of neurons in the output layer is 79 as the proposed
system is designed to recognize English alphabets and digits.
Post-Processing:
• Post-Processing stage is the final stage of the proposed recognition
system.
• It prints the corresponding recognized character in the structured text
form.
UML DIAGRAM:
USE CASE DIAGRAM:
Upload
Image
Cance
l <<include User
Convert Image-
Initialize >> Gray Scale
<<include
>>
Pre-Process Image
<<include Gray Scale to
>> Binary format
Syste Recogniz
m e
Normalizatio
Generate n
Output
STATUS:
o Literature survey is completed.
o Studied various datasets for a fair selection of a feasible
dataset which could be applied for predicting all the
activities of the user.
o Installed required packages.
o Completed Model building
Thank You
