Hand-Written and Machine-Printed Text Classification in Architecture, Engineering & Construction Documents

In AEC (Architecture, Engineering & Construction) industry, drawing documents are used as a blueprint to facilitate the construction process. It is also represented as a graphical language that communicates ideas and information from one mind to another. In AEC documents, text is present in Machine-printed and hand-written format. Since the algorithms for recognition of machine-printed and hand-written texts are different, it is important to distinguish between these two types of texts before sending the document to respective recognition system. In this paper we proposed a novel approach for the classification machine-printed and hand-written text from AEC Documents. Before Classification Hand-Written and Machine-Printed text from the documents our system used some preprocessing which includes binarization, text graphics separation and word segmentation. The Words are segmented based on certain structural properties of Isothetic Covers (IC) tightly enclosing the words in a document. The grid size properties of IC are selected by some statistical analysis of connected component of the document. Then Word level Gabor Filter based features are extracted with spooling information for classification. A standard classifier based on SVM is used to classify the text. This task is performed at word level of AEC documents and we achieved an overall accuracy of 98.45%.

• Publication year

  2018

• Venue

  International Conference on Frontiers in Handwriting Recognition

• Publication date

  2018-08-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/ICFHR-2018.2018.00101
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A System for Handwritten and Printed Text Classification

  2017cited by this paper
• Handwritten and printed text distinction by using stroke thickness features

  2017cited by this paper
• A Survey on Techniques of Separation of Machine Printed Text and Handwritten Text

  2013cited by this paper
• A Survey on Techniques of Separation of Machine Printed Text and Handwritten Text

  2013cited by this paper
• A System for Handwritten and Machine-Printed Text Separation in Bangla Document Images

  2012cited by this paper
• Efficient Word Segmentation and Baseline Localization in Handwritten Documents Using Isothetic Covers

  2011cited by this paper
• Signature Segmentation from Machine Printed Documents Using Conditional Random Field

  2011cited by this paper
• Construction of isothetic covers of a digital object: A combinatorial approach

  2010cited by this paper
• Structural handwritten and machine print classification for sparse content and arbitrary oriented document fragments

  2010cited by this paper
• Markov Random Field Based Text Identification from Annotated Machine Printed Documents

  2009cited by this paper
• Multi-scale Structural Saliency for Signature Detection

  2007cited by this paper
• Segmentation and labeling of documents using conditional random fields

  2007cited by this paper
• Identifying Handwritten Text in Mixed Documents

  2006cited by this paper
• Machine-printed from handwritten text discrimination

  2004cited by this paper
• Machine printed text and handwriting identification in noisy document images

  2004cited by this paper
• Classification of machine-printed and handwritten addresses on Korean mail piece images using geometric features

  2004cited by this paper
• Characterizing and distinguishing text in bank cheque images

  2002cited by this paper
• Separating handwritten material from machine printed text using hidden Markov models

  2001cited by this paper
• Automatic separation of machine-printed and hand-written text lines

  1999influential reference
• A system for machine-written and hand-written character distinction

  1995cited by this paper
• A Threshold Selection Method from Gray-Level Histograms

  1979cited by this paper
• Book Reviews

  1893cited by this paper
• This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Signature Detection and Matching

  year unknowncited by this paper

• Information Extraction From Blood Test PDFs: Systematic Review and Case Study

  2025cites this paper
• Automatic digitalization of railway interlocking systems engineering drawings based on hybrid machine learning methods

  2025cites this paper
• Optimizing Text Recognition in Mechanical Drawings: A Comprehensive Approach

  2025cites this paper
• Ontology-based reasoning in automatic floor plan analysis

  2025cites this paper
• Image Processing and Natural Language Processing Based Digitalized Document Generator

  2023cites this paper
• Optical character recognition on engineering drawings to achieve automation in production quality control

  2023influential citation
• AI-Based Method for Frame Detection in Engineering Drawings

  2023cites this paper
• Extraction of dimension requirements from engineering drawings for supporting quality control in production processes

  2021cites this paper
• Deep Learning for Text Detection and Recognition in Complex Engineering Diagrams

  2020cites this paper
• Discrimination between Handwritten and Computer-Generated Texts using a Distribution of Patch-Wise Font Features

  2019cites this paper