Segmentation of english Offline handwritten cursive scripts using a feedforward neural network

Sharma, Manoj Kumar; Dhaka, Vijay Pal

doi:10.1007/s00521-015-1940-x

Cited by 25 publications

(7 citation statements)

References 23 publications

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“…Garain and Chaudhuri [8] explored the techniques for extracting overlapped segments in scanned Devnagari and Bangla scripts using multifactorial analysis. In the recognition-based category, Sharma and Dhaka [9] worked on the trace-based framework and pixel plot to segment the characters using a feed-forward neural network.…”

Section: Related Workmentioning

confidence: 99%

Data extraction from scanned invoice documents in multiple languages

Aggarwal,

Patra,

Sinha

et al. 2023

International Workshop on Signal Processing and Machine Learning (WSPML 2023)

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Section: Related Workmentioning

confidence: 99%

Data extraction from scanned invoice documents in multiple languages

Aggarwal,

Patra,

Sinha

et al. 2023

International Workshop on Signal Processing and Machine Learning (WSPML 2023)

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“…Optical character recognition (OCR) technology has been developed for a wide range of languages and scripts, including English [8], French [9], Spanish [10], German [11], Chinese [12], Japanese [13], Korean [14], Arabic [15], Hebrew [16], Russian [17], Greek [18], Latin [19], Devanagari [20], Thai [21], and Vietnamese [22]. OCR technology has been developed for any language or script that has a written form and is used for storing and communicating information electronically.…”

Section: : Introductionmentioning

confidence: 99%

Pashto handwritten character recognition based on Generative Adversarial Network (GAN) and Deep learning

ullah

2023

Preprint

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In the field of machine learning, various models are used to train and test machines to perform a variety of tasks, including data analysis, computer vision, speech recognition, pattern matching, pattern recognition, and natural language processing. Computer vision is an important area where deep learning architectures, GANs, and transfer learning are applied. In the field of pattern matching and pattern recognition, research based on deep learning models has attracted significant attention from researchers due to their excellent performance. Pashto is an ancient and historical language spoken in Pakistan and Afghanistan. Optical character recognition (OCR) systems using Generative Adversarial Network (GAN) have been developed for several cursive languages, such as Chinese, Urdu, and Japanese, but no study has been done yet to recognize Pashto handwritten characters using the GAN network architecture. In this study, we used a Generative Adversarial Network (GAN) to generate additional images of handwritten characters in order to help improve the accuracy of a recognition system for handwritten characters. We used a dataset of 43,000 images representing 43 different classes of Pashto characters, which was collected from GitHub repositories and is available for other researchers to use. We employed two deep transfer models, ResNet50 and VGGNet16, to recognize the Pashto handwritten characters and evaluated their performance. The results showed that VGGent16 achieved 98.39% testing accuracy while ResNet50 achieved 99.24% testing accuracy. From the experimental result found that ResNet50 achieved a higher accuracy rate than VGGNet16 when tested on the dataset of Pashto characters. Furthermore, the accuracy rate achieved by ResNet50 was higher than the accuracy rates reported in previous studies on Pashto character recognition.

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“…The capacity of the machine to transform handwritten documents obtained by an imaging device into a machine understandable format is called off-line HCR (handwritten character recognition). This off-line process is the major segment of several real time applications like mail sorting in posts, data entry and bank cheque processing [1]. One essential process of document image analysing is automatically reading the text data from the document image.…”

Section: Introductionmentioning

confidence: 99%

Dataset Generation for Gujarati Language Using Handwritten Character Images

Suthar

Thakkar

2023

Preprint

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In pattern recognition, the handwritten character recognition (HCR) is considered as the classical challenge. Due to the unavailability of the dataset for different languages, it is complex to train the recognition system. In particular, the benchmark dataset for HCR in the Gujarati language is limited. To overcome this challenge, a proper dataset is required for experimentation. Hence, this work introduces dataset generation for the Gujarati language using pre-processing and classification techniques. Initially, the handwritten data is collected from various native Gujarati writers. In this work, there are three processes carried out to generate the dataset. They are pre-processing, segmentation and classification. Initially, the pre-processing stages like a selection of image, noise removal, normalization, conversion of integer value to double, grayscale image into a binary image, dimensionality reduction, and vector conversation are performed. Then, the pre-processed image is segmented using line segmentation, character segmentation and word segmentation. Then, for testing and training, the data are transformed into CSV file format (for converting the information to numbers). Finally, the data are classified using a Convolutional neural network (CNN). The kappa and FPR values achived by the CNN are 0.981 and0.189.

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Segmentation of english Offline handwritten cursive scripts using a feedforward neural network

Cited by 25 publications

References 23 publications

Data extraction from scanned invoice documents in multiple languages

Data extraction from scanned invoice documents in multiple languages

Pashto handwritten character recognition based on Generative Adversarial Network (GAN) and Deep learning

Dataset Generation for Gujarati Language Using Handwritten Character Images

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