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International Journal of Forensic Science

Volume  7, Issue 1, January–June 2024, Pages 51-56
 

Original Article

Offline Script Identification from Gujrati Handwritten Documents

Akash Sharma1 , Chhote Raja Patle2 , Anuwanshi Sharma3 , Anita Yadav4

: 1 M.Sc Student, 2 Assistant Professor, 4 Associate Professor, Department of Forensic Science, Sanjeev  Agrawal Global Educational University, Bhopal 462022, Madhya Pradesh, India, 3 PhD Scholar, Department of Forensic Science, Galgotias University, Greater Noida 203201, Uttar Pradesh, India.

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Abstract

This study focuses on offline script identification of handwritten Gujarati script documents  using Optical Character Recognition (OCR) techniques. The goal is to develop an efficient  system capable of accurately identifying the Gujarati script from handwritten documents. The  process begins with the collection of a diverse dataset of offline handwritten Gujarati script  documents. The dataset includes various handwriting styles to ensure the model's adaptability.  Ground truth labels are annotated for training and evaluation purposes.  Preprocessing techniques are employed to enhance the image quality of the handwritten  documents. These techniques involve noise removal, image resizing, and normalization, resulting  in clearer and standardized input for the subsequent steps. OCR techniques are then applied to perform the script identification task. These techniques involve the extraction of features  and patterns specific to the Gujarati script from the pre-processed images. Machine learning algorithms, such as Support Vector Machines (SVM) or Convolutional Neural Networks (CNN), are trained on the extracted features to learn the script identification patterns. The trained model is evaluated using standard performance metrics, including accuracy, precision, recall, and F1 score. The dataset is divided into training and testing sets to assess the model's effectiveness in identifying the Gujarati script. Once the model is trained and evaluated, it can be deployed for practical use. Given an input handwritten document, the OCR system utilizes its learned patterns to accurately identify and classify the Gujarati script. Overall, this study presents a concise approach to offline script identification of handwritten Gujarati script documents using OCR techniques. The proposed system shows promise in accurately reorganizing the Gujarati script, paving the way for further advancements in this field.


Keywords : Offline Script Identification; Handwritten; Gujarati Script; Document; OCR; Optical Character Recognition; Dataset; Preprocessing; Feature Extraction; Machine Learning; Support Vector Machines; SVM; Convolutional Neural Networks; CNN; Performance Evaluation; Accuracy; Precision; Recall; F1 Score.
Corresponding Author : Anita Yadav,