Multi-experts for touching digit string recognition

84.6 % of touching digit strings have only two digits touching, 12.3 % have three digits touching, and 3.1% have more than three digits touching. We present a multiexperts approach to recognize touching digit pairs (TDP) and touching digit triples (TDT). We combine holistic and traditional segmentation methods. 25,686 TDP training samples and 2778 TDP testing samples collected from USPS mail are used in our experiment. Holistic method outperforms the traditional segmentation based methods. The multi-experts combination has the best performance, a correct rate of 91.1 % on TDP. 1

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

ミャンマー語テキストの形式手法による音節分割、正規化と辞書順排列

国立大学法人長岡技術科学大

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