Segmentation of touching and fused Devanagari characters

The reading process has been widely studied and there is a general agreement among researchers that knowledge in different forms and at different levels plays a vital role. This is the underlying philosophy of the Devanagari document recognition system described in this work. The knowledge sources we use are mostly statistical in nature or in the form of a word dictionary tailored specifically for optical character recognition (OCR). We do not perform any reasoning on these. However, we explore their relative importance and role in the hierarchy. Some of the knowledge sources are acquired a priori by an automated training process while others are extracted from the text as it is processed. A complete Devanagari OCR system has been designed and tested with real-life printed documents of varying size and font. Most of the documents used were photocopies of the original. A performance of approximately 90% correct recognition is achieved.

http://www.iitk.ac.in/ime/veena/papers/inte.pdf

Integrating knowledge sources in Devanagari text recognition system

The paper presents a schema for the description of shapes of Devanagari characters and its application in their recognition. It exploits certain features of the script in both reducing the search space and creating a reference with respect to which correspondence could be established, during the matching process. The description prototypes are constructed using the real-life script after segmentation so that the aberrations introduced during the inevitable process of segmentation get accounted for in the description. This has been tested on printed Devanagari text with a success of approximately 70% without any post-processing and 88% correct recognition with the help of a word dictionary.

On how to describe shapes of Devanagari characters and use them for recognition

We took a multi-resolution approach to the signature verification problem. The top-level representation of signatures was the global geometric features. A multi-resolution representation of signatures was obtained using the wavelet transformation. We built VQ and network classifiers to demonstrate the advantages of the multi-resolution approach. High verification rates were achieved based on a limited database. >

/pdf/a-multiresolution-approach-to-computer-verification-of-oibrxayqnm.pdf

A multiresolution approach to computer verification of handwritten signatures

Feature extraction is one of the fundamental problems of character recognition. The performance of character recognition system is largely depending on proper feature extraction and correct classifier selection. In this article, a rapid feature extraction method is proposed and named as Celled Projection (CP) that compute the projection of each section formed through partitioning an image. The recognition performance of the proposed method is compared with other widely used feature extraction methods that are intensively studied for many different scripts in literature. The experiments have been conducted using Bangla handwritten numerals along with three different well known classifiers which demonstrate comparable results including 94.12% recognition accuracy using celled-projection.

Rapid feature extraction for Bangla handwritten digit recognition

A method of recognizing handprinted characters

Based on the recognition requirement to suppress types of noise created in the conversion process of handwritten characters into binary patterns in our optical character recognition, an algorithm of processing digital character noise is presented. The software and hardware of the algorithm is described.

Yan Guang-Yi

Papers

A method of recognizing handprinted characters

A method of processing digital character noise