Connected and degraded text recognition using hidden Markov model Abstract   We have applied a Hidden Markov Model (HMM) and levelbuilding dynamic programming algorithm to the problem of robust machine recognition of connected and degraded characters forming words in a poorly printed text. The recognition system consists of preprocessing, subcharacter segmentation and feature extraction, followed by supervised learning or recognition. A structural analysis algorithm is used to segment a word into subcharacter segments irrespective of the character boundaries, and to identify the primitive features in each segment such as strokes and arcs. The states of the HMM for each character are statistically represented by the subcharacter segments, and the state characteristics are obtained by determining the state probability functions based on the training samples. In order to recognize an unknown word, subcharacter segmentation and feature extraction are performed and the transition probabilities between character models are used for the transition between characters in the string. A levelbuilding dynamic programming algorithm combines segmentation and recognition of the word in one operation and chooses the best probable grouping of characters for recognition of an unknown word. The computer experiments demonstrate the robustness and effectiveness of the new system for recognizing words formed by degraded and connected characters.

A hierarchical deformation model for online cursive script recognition Abstract   For the character recognition problem, it is known that the prior knowledge about the structure of patterns can be utilized as a guidance to obtain an accurate match efficiently. Naturally, the strokes, which are the primitive components of Chinese characters, play an important role to guide the correct recognition. Based on this highlevel structural information, a hierarchical deformation model is proposed to describe the deformation of online cursive Chinese characters. The new approach consists of two levels of match processes. First, the attributed string editing algorithm matches two sequences of turn points extracted from the input and the reference characters to determine the stroke matches. Next, the constrained parabola transformation is used to reduce the difference between the matched strokes appropriately. Experimental results show that the hierarchical deformation model is a quite accurate approximation to the deformation of cursive Chinese characters with much lower computational cost. Furthermore, the distance measure between deformable characters derived in this paper is robust enough to greatly improve the performance of practical recognition systems.