Data Extraction from Hand-filled Form using Form Template

Database is very vital for taking the day to day decision and in the long run it helps in formulation of policies, strategies of an organization. Numerous efforts, time and money are spent to get, store and process the data. To get the data from a user, an interface is designed which is known as form. The forms may vary from paper based to online. Manually processing paper based form is prone to errors. Therefore, it will be useful to deploy automated systems for reading data from paper based forms and storing it in the database. Further, this data can be modified, processed and analyzed. In this paper, we have proposed a method to extract data from hand-filled pre-designed form based on form templates. DOI: 10.17762/ijritcc2321-8169.15084

Recognition and identification of form document layouts

In this thesis, a hierarchical tree representation is introduced to represent the logical structure of a form document. But different forms might have the same logical structure, so the representation will be ambiguous. In this thesis, an improvement is proposed to solve the ambiguity problem by using the physical information of the blocks. To fulfill the application of hierarchical tree representation and extract the physical information of blocks, a pixel tracing approach is used to extract form layout structures from form documents. Compared with Hough transform, the pixel tracing algorithm requires less computation. This algorithm has been tested on 50 different table forms. It effectively extracts all the line information required for the hierarchical tree representation, represents the form by a hierarchical tree, and distinguishes the different forms. The algorithm applies to table form documents

Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy