Scholarlink

<p>Automated recognition of handwritten text on bank cheques is crucial for streamlining financial transactions and reducing manual errors. However, traditional systems often encounter two significant challenges: differentiating overlapping handwritten and printed text and restoring faded or partially damaged handwriting. This paper presents a multi-stage deep learning framework that effectively addresses these issues to extract essential cheque fields accurately. The process begins with preprocessing to remove noise and enhance the cheque image. Overlapping regions of handwritten and printed text are identified and segmented using a hybrid approach that combines pseudo letters with height-based segmentation and intelligent character recognition. Key handwritten elements such as the date, signature, name, and amount are then extracted using a fine-tuned Nanonet model. For severely faded handwriting, a restoration stage employing edge detection, contour construction, and texture inpainting is implemented. Finally, a novel sigmoidal growing cosine intermap pooling-based convolutional neural network is used to classify cheques as genuine or forged, achieving a classification accuracy of 98.79%. Experimental results demonstrate that the proposed framework outperforms existing methods in terms of segmentation accuracy, recognition robustness, and classification performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-stage deep learning framework for robust recognition of overlapping and faded handwritten text in bank cheques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月3日

查看全文
  • Hitesh Chaitanyaswami,
  • Ashwin Dobariya,
  • Sailesh Iyer,
  • Chin-Ling Chen,
  • Ling-Chun Liu,
  • Mueen Uddin,
  • Saddam Hussain

摘要

Automated recognition of handwritten text on bank cheques is crucial for streamlining financial transactions and reducing manual errors. However, traditional systems often encounter two significant challenges: differentiating overlapping handwritten and printed text and restoring faded or partially damaged handwriting. This paper presents a multi-stage deep learning framework that effectively addresses these issues to extract essential cheque fields accurately. The process begins with preprocessing to remove noise and enhance the cheque image. Overlapping regions of handwritten and printed text are identified and segmented using a hybrid approach that combines pseudo letters with height-based segmentation and intelligent character recognition. Key handwritten elements such as the date, signature, name, and amount are then extracted using a fine-tuned Nanonet model. For severely faded handwriting, a restoration stage employing edge detection, contour construction, and texture inpainting is implemented. Finally, a novel sigmoidal growing cosine intermap pooling-based convolutional neural network is used to classify cheques as genuine or forged, achieving a classification accuracy of 98.79%. Experimental results demonstrate that the proposed framework outperforms existing methods in terms of segmentation accuracy, recognition robustness, and classification performance.