Abstract
<span id="orcid-id" class="orcid-id-https">These days there is a huge demand in “storing the information available in paper documents into a computer storage disk”. Digitizing manual filled forms lead to handwriting recognition, a process of translating handwriting into machine editable text. The main objective of this research is to to create an Android application able to recognize and predict the output of handwritten characters by training a neural network model. This research will implement deep neural network in recognizing handwritten text recognition especially to recognize digits, Latin / Alphabet and Hiragana, capture an image or choose the image from gallery to scan the handwritten text from the image, use the live camera to detect the handwritten text real – time without capturing an image and could copy the results of the output from the off-line recognition and share it to other platforms such as notes, Email, and social media. </span>
Highlights
There is a vast amount of history about handwriting because it has been around for a very long time, just like our writing today, it is used to store information and communicate with others
This research will implement deep neural network in recognizing handwritten text recognition especially to recognize digits, Latin / Alphabet and Hiragana, capture an image or choose the image from gallery to scan the handwritten text from the image, use the live camera to detect the handwritten text real – time without capturing an image and could copy the results of the output from the off-line recognition and share it to other platforms such as notes, Email, WA etc
This application implement deep neural networks in Python and trained using Keras and TensorFlow, deep neural network search is used in this application to train the model to recognize handwritten images
Summary
There is a vast amount of history about handwriting because it has been around for a very long time, just like our writing today, it is used to store information and communicate with others. Different languages have very different characteristics of their alphabets which form the basis of this written text, and in some languages alphabets are writen isolated from each other (e.g., Thai and Japanese) in some other cases they are cursive and sometimes the characters are connected with each other (e.g., Arabics) All of these challenges were recognized by many researchers [1 - 3]. As described in [4], most of the recent Japanese character recognition approaches, both for handwritten and printed text, eitheruse soft computing based approaches for classification, or, image shape/morphology characteristics for classification. This research will implement deep neural network in recognizing handwritten text recognition especially to recognize digits, Latin / Alphabet and Hiragana, capture an image or choose the image from gallery to scan the handwritten text from the image, use the live camera to detect the handwritten text real – time without capturing an image and could copy the results of the output from the off-line recognition and share it to other platforms such as notes, Email, WA etc
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