646 research outputs found
Handwritten Digit Recognition and Classification Using Machine Learning
In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy
Feedback Based Architecture for Reading Check Courtesy Amounts
In recent years, a number of large-scale applications continue to rely heavily on the use of paper as the
dominant medium, either on intra-organization basis or on inter-organization basis, including paper
intensive applications in the check processing application. In many countries, the value of each check is
read by human eyes before the check is physically transported, in stages, from the point it was presented
to the location of the branch of the bank which issued the blank check to the concerned account holder.
Such process of manual reading of each check involves significant time and cost. In this research, a new
approach is introduced to read the numerical amount field on the check; also known as the courtesy
amount field. In the case of check processing, the segmentation of unconstrained strings into individual
digits is a challenging task because one needs to accommodate special cases involving: connected or
overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a
neighboring digit. The system described in this paper involves three stages: segmentation, normalization,
and the recognition of each character using a neural network classifier, with results better than many other
methods in the literaratu
Handwritten Bank Check Recognition of Courtesy Amounts
In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use
of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the
issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into
individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are
physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages:
segmentation of the string into individual digits, normalization, recognition of each character using a neural network
classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture
that incorporates multiple approaches to provide high recognition rates
Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform
In this research, off-line handwriting recognition system for Arabic alphabet is
introduced. The system contains three main stages: preprocessing, segmentation and
recognition stage. In the preprocessing stage, Radon transform was used in the design
of algorithms for page, line and word skew correction as well as for word slant
correction. In the segmentation stage, Hough transform approach was used for line
extraction. For line to words and word to characters segmentation, a statistical method
using mathematic representation of the lines and words binary image was used.
Unlike most of current handwriting recognition system, our system simulates the
human mechanism for image recognition, where images are encoded and saved in
memory as groups according to their similarity to each other. Characters are
decomposed into a coefficient vectors, using fast wavelet transform, then, vectors,
that represent a character in different possible shapes, are saved as groups with one
representative for each group. The recognition is achieved by comparing a vector of
the character to be recognized with group representatives.
Experiments showed that the proposed system is able to achieve the recognition task
with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a
single character in a text of 15 lines where each line has 10 words on average
Machine Learning for Handwriting Recognition
With the knowledge of current data about particular subject, machine learning tries to extract hidden information that lies in the data. By applying some mathematical functions and concepts to extract hidden information, machine learning can be achieved and we can predict output for unknown data. Pattern recognition is one of the main application of ML. Patterns are usually recognized with the help of large image data-set. Handwriting recognition is an application of pattern recognition through image. By using such concepts, we can train computers to read letters and numbers belonging to any language present in an image. There exists several methods by which we can recognize hand-written characters. We will be discussing some of the methods in this paper
An Integrated architecture for recognition of totally unconstrained handwritten numerals
Reprint. Reprinted from the International journal of pattern recognition and artificial intelligence. Vol. 7, no. 4 (1993) "January 1993."Includes bibliographical references (p. 127-128).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Amar Gupta ... [et al.
Data Generation for Post-OCR correction of Cyrillic handwriting
This paper introduces a novel approach to post-Optical Character Recognition
Correction (POC) for handwritten Cyrillic text, addressing a significant gap in
current research methodologies. This gap is due to the lack of large text
corporas that provide OCR errors for further training of language-based POC
models, which are demanding in terms of corpora size. Our study primarily
focuses on the development and application of a synthetic handwriting
generation engine based on B\'ezier curves. Such an engine generates highly
realistic handwritten text in any amounts, which we utilize to create a
substantial dataset by transforming Russian text corpora sourced from the
internet. We apply a Handwritten Text Recognition (HTR) model to this dataset
to identify OCR errors, forming the basis for our POC model training. The
correction model is trained on a 90-symbol input context, utilizing a
pre-trained T5 architecture with a seq2seq correction task. We evaluate our
approach on HWR200 and School_notebooks_RU datasets as they provide significant
challenges in the HTR domain. Furthermore, POC can be used to highlight errors
for teachers, evaluating student performance. This can be done simply by
comparing sentences before and after correction, displaying differences in
text. Our primary contribution lies in the innovative use of B\'ezier curves
for Cyrillic text generation and subsequent error correction using a
specialized POC model. We validate our approach by presenting Word Accuracy
Rate (WAR) and Character Accuracy Rate (CAR) results, both with and without
post-OCR correction, using real open corporas of handwritten Cyrillic text.
These results, coupled with our methodology, are designed to be reproducible,
paving the way for further advancements in the field of OCR and handwritten
text analysis. Paper contributions can be found in
https://github.com/dbrainio/CyrillicHandwritingPOCComment: 17 pages, 27 figures, 6 tables, 26 reference
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