The demand for daily form digitization requires extensive manual effort. This paper presents an efficient pipeline for digitizing Indic handwritten forms, minimizing human intervention. We validate the pipeline using Hindi and Bengali forms, creating a dedicated test set, IIIT-Indic-Form-Test. Our approach enables form capture and orientation alignment via smartphone, extracting printed and handwritten fields with OCR enhanced by template annotations. A predefined word list supports post-processing for fields like name, state, and country. We compare our pipeline with tools like Google Parser and Microsoft Azure and conduct ablation studies on form style, rotation, and handwriting variations. A GUI-based application for digitization is also developed. The code and model are publicly available at  https://github.com/shaoncvit/Indic_Handwritten_Form_dataset .

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Towards Digitizing Filled Indic Handwritten Forms

  • Shaon Bhattacharyya,
  • Ajoy Mondal,
  • C. V. Jawahar

摘要

The demand for daily form digitization requires extensive manual effort. This paper presents an efficient pipeline for digitizing Indic handwritten forms, minimizing human intervention. We validate the pipeline using Hindi and Bengali forms, creating a dedicated test set, IIIT-Indic-Form-Test. Our approach enables form capture and orientation alignment via smartphone, extracting printed and handwritten fields with OCR enhanced by template annotations. A predefined word list supports post-processing for fields like name, state, and country. We compare our pipeline with tools like Google Parser and Microsoft Azure and conduct ablation studies on form style, rotation, and handwriting variations. A GUI-based application for digitization is also developed. The code and model are publicly available at  https://github.com/shaoncvit/Indic_Handwritten_Form_dataset .