A priori algorithm for sub-category classification analysis of handwriting

Cha, Sung-Hyuk; Srihari, S.N.

doi:10.1109/icdar.2001.953940

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…Currently, deep learning technology is widely used in handwritten image gender classification, but there is relatively little research on using deep learning technology for hand-written image age classification. For example, Cha et al [27] trained an ANN on a capital letter dataset to classify demographic subcategories such as gender, handedness, and age group and used reinforcement learning techniques such as bagging and boosting to extract features and classify them using forward neural networks. Irina Rabaev et al [28] proposed a deep neural network model called Bilinear Convolutional Neural Network (B-CNN) for automatic age and gender classification of handwritten images.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Age Estimation from Handwriting: A Deep Learning Approach with Attention Mechanisms

Zhao,

Wu,

Chen

2024

ijacsa

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Currently, age estimation is a hot research topic in the field of forensic biology. Age estimation methods based on facial or brain features are easily affected by external factors. In contrast, handwriting analysis is a more reliable method for age estimation. This paper aims to improve the accuracy and efficiency of age prediction using handwriting analysis by proposing a novel method that integrates a coordinate attention mechanism in a deep residual network (CA-ResNet). This method can more accurately capture important features in the input handwritten images while reducing the number of model parameters, thereby improving the accuracy (Acc) and efficiency of the model for age estimation. The proposed method is evaluated on standard handwriting datasets and the created dataset, and it is compared with the current state-of-the-art methods. The results show that the method consistently outperforms others, achieving an accuracy of 79.60% on the IAM handwriting dataset, with a 6.31% improvement over other methods.

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Section: Related Workmentioning

confidence: 99%

Enhancing Age Estimation from Handwriting: A Deep Learning Approach with Attention Mechanisms

Zhao,

Wu,

Chen

2024

ijacsa

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“…Then, different persons varying in age, sex and educational qualification (see Figure 8(a-c)) were requested to fill up the datasheets using black ink gel pen. The choice of such variations of writers incorporates varieties in writing styles (Cha & Srihari (2001); Siddiqi et al (2015); Bouletreau et al (1997)). A portion of filled-in datasheet is shown in Figure 9.…”

Section: Data Preparationmentioning

confidence: 99%

A Holistic Approach for Handwritten Hindi Word Recognition

Nasipuri¹,

Sarkar

Malakar

et al. 2017

International Journal of Computer Vision and Image Processing

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Holistic word recognition attempts to recognize the entire word image as a single pattern. In general, it performs better than segmentation based word recognition model for known, fixed and small sized lexicon. The present work deals with recognition of handwritten words in Hindi in holistic way. Features like area, aspect ratio, density, pixel ratio, longest run, centroid and projection length are extracted either from entire word image or from the hypothetically generated sub-images of the same. An 89-elements feature vector has been designed to represent each word in the feature space and five different classifiers have been used for measuring recognition performances. Considering the complexities of Hindi characters, the technique shows an impressive result using a Multilayer Perceptron (MLP) based classifier. Moreover, the technique shows scale and rotation invariant nature to a significant extent.

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Handwriting style classification

Dehkordi

Sherkat

Allen

2003

International Journal on Document Analysis and Recognition

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This paper describes an independent handwriting style classifier that has been designed to select the best recognizer for a given style of writing. For this purpose a definition of handwriting legibility has been defined and a method has been implemented that can predict this legibility. (legible/illegible), 65.5% (legible/middle) and 90.5% (middle/illegible) correct classification for two classes. For the three-classes legibility classification the rate of correct classification is 67.33% using PNN classifier.

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A priori algorithm for sub-category classification analysis of handwriting

Cited by 8 publications

References 3 publications

Enhancing Age Estimation from Handwriting: A Deep Learning Approach with Attention Mechanisms

Enhancing Age Estimation from Handwriting: A Deep Learning Approach with Attention Mechanisms

A Holistic Approach for Handwritten Hindi Word Recognition

Handwriting style classification

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