Recognition assistance treating errors in texts acquired from various recognition processes

Abstract

Texts acquired from recognition sources-continuous speech/handwriting recognition and OCR-generally have three types of errors regardless of the characteristics of the source in particular. The output of the recognition process may be (1) poorly segmented or not segmented at all; (2) containing underspecified symbols (where the recognition process can only indicate that the symbol belongs to a specific group), e.g. shape codes; (3) containing incorrectly identified symbols. The project presented in this paper addresses these errors by developing of a unified linguistic framework called the MorphoLogic Recognition Assistant that provides feedback and corrections for various recognition processes. The framework uses customized morpho-syntactic and syntactic analysis where the lexicons and their alphabets correspond to the symbol set acquired from the recognition process. The successful framework must provide three services: (1) proper disambiguated segmentation, (2) disambiguation for underspecified symbols, (3) correction for incorrectly recognized symbols. The paper outlines the methods of morpho-syntactic and syntactic post-processing currently in use.

Can Morphological Analyzers Improve the Quality of Optical Character Recognition?

“…ere is a large body of literature on spelling correction for morphologically rich languages, for example [5] and [6], and similar approaches have been successfully applied to OCR post-processing, for example [7,8,9]. In our work, we wanted to improve the language model instead of using post-processing to correct errors, because post-processing cannot in principle give as good results as improved language modeling.…”

Ambiguity and Contradiction From a Morpho-Syntactic Prototype Perspective

“…Some of the work in this area is related to lexical [14] [15], syntactic [16], morphologic [17], phonemic [18] [19], semantic [20] [21] levels or certain combination of them [22] [23]. In other prototypes as CLICK-TALP this task is performed manually [24] [25].…”

Intelligent Chatter Bot for Regulation Search

“…As a last example, there is a tool for morphology [16] that performs morphological and syntactic analysis with disambiguated segmentation (splits text into segments according to its coherence), special symbol disambiguation (used for sounds not related to words) and error correction for words misunderstood.…”