Authors :
Naiknaware Reshma; Nitin M.Shivale; Patil Shrishail; Dr. Bhandari Gayatri
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/472z6svv
Scribd :
https://tinyurl.com/25r5jz2j
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR275
Abstract :
Currency classification and Image to Text
OCR are essential technologies that find applications in
various domains, including finance, retail, and
automation. The approach outlined in this paper has the
potential to detect currencies from multiple countries.
However, for practical implementation purposes, the
focus is solely on Indian paper currencies. This system
offers the advantage of convenient currency checking at
any time and location, leveraging Convolutional Neural
Networks (CNN) for effective implementation. Extensive
testing was conducted on each denomination of Indian
currency, resulting in an impressive 95% accuracy rate.
To further refine accuracy, a classification model was
developed, incorporating all pertinent factors discussed
in the paper. Notably, the unique features of paper
currency play a pivotal role in the recognition process.
By emphasizing these elements and harnessing CNN
technology, the proposed system demonstrates significant
promise in accurately detecting and validating Indian
paper currencies. It stands poised to serve various
applications effectively. On the other hand, Image to
Text OCR focuses on extracting text from images,
enabling the conversion of non- editable documents into
searchable and editable formats.
Both technologies contribute to automation and
efficiency in handling diverse visual information. Optical
Character Recognition (OCR) is a technologydesigned to
recognize and interpret both printed and handwritten
characters by scanning text images. This process involves
segmenting the text image into regions, isolating
individual lines, and identifying each character along
with its spacing. After isolating individual characters
from the text image, the system conducts an analysis of
their texture and topological attributes. This involves
examining corner points, unique characteristics of
various regions within the characters, and calculating
the ratio of character area to convex area Prior to
initiating recognition, the system creates templates that
store the distinctive features of uppercase and lowercase
letters, digits, and symbols.
These templates serve as reference models for
comparison during the recognition phase. During
recognition, the system matches the extracted
character's texture and topological Features with those
stored in the templates to determine the exact character.
This matching process involves comparing features of
the extracted character with templates of all characters,
measuring similarity, and ultimately recognizing the
character accurately.
Keywords :
Currency Recognition, CNN, OCR, Deep Learning.
Currency classification and Image to Text
OCR are essential technologies that find applications in
various domains, including finance, retail, and
automation. The approach outlined in this paper has the
potential to detect currencies from multiple countries.
However, for practical implementation purposes, the
focus is solely on Indian paper currencies. This system
offers the advantage of convenient currency checking at
any time and location, leveraging Convolutional Neural
Networks (CNN) for effective implementation. Extensive
testing was conducted on each denomination of Indian
currency, resulting in an impressive 95% accuracy rate.
To further refine accuracy, a classification model was
developed, incorporating all pertinent factors discussed
in the paper. Notably, the unique features of paper
currency play a pivotal role in the recognition process.
By emphasizing these elements and harnessing CNN
technology, the proposed system demonstrates significant
promise in accurately detecting and validating Indian
paper currencies. It stands poised to serve various
applications effectively. On the other hand, Image to
Text OCR focuses on extracting text from images,
enabling the conversion of non- editable documents into
searchable and editable formats.
Both technologies contribute to automation and
efficiency in handling diverse visual information. Optical
Character Recognition (OCR) is a technologydesigned to
recognize and interpret both printed and handwritten
characters by scanning text images. This process involves
segmenting the text image into regions, isolating
individual lines, and identifying each character along
with its spacing. After isolating individual characters
from the text image, the system conducts an analysis of
their texture and topological attributes. This involves
examining corner points, unique characteristics of
various regions within the characters, and calculating
the ratio of character area to convex area Prior to
initiating recognition, the system creates templates that
store the distinctive features of uppercase and lowercase
letters, digits, and symbols.
These templates serve as reference models for
comparison during the recognition phase. During
recognition, the system matches the extracted
character's texture and topological Features with those
stored in the templates to determine the exact character.
This matching process involves comparing features of
the extracted character with templates of all characters,
measuring similarity, and ultimately recognizing the
character accurately.
Keywords :
Currency Recognition, CNN, OCR, Deep Learning.
