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After the recognition process is completed the model name is read from the Master Label File (.mmf) and the associated Unicode character for the recognized model is written to the output file. All these files are constructed according to HTK Toolkit understandable format. mmf file where the description of each HMM model is written. HVite uses feature file of the segmented character image where the features are written in a specified format, the word network that describes the allowable word sequence build up from task grammar, the dictionary that define each character or word, the entire list of HMMs and the. The Recognition process invokes the recognition tool HVite of the HTK toolkit. The model creation task is automatically performed by invoking HInit tool of the HTK toolkit. The model creating involves dynamically choosing a prototype HMM model and creates a model using the prototype HMM and the extracted feature data. We created separate HMM model for each segmented character image.
![hidden markov model matlab optical character recognition hidden markov model matlab optical character recognition](https://www.eurekaselect.com/images/graphical-abstract/cst/15/2/003.jpg)
Training is performed over the calculated features of each minimally segmented character image. We consider the number of frames and DCT calculated values of each frame as the features for each character. Then we performed Discrete Cosine Transform (DCT) calculation over each frames. First we divide the character image into several frames using a certain frame length. The Feature Extraction task involves the extraction of meaningful characteristics of a minimally segmented character. In this step we put our effort up to minimal segmentation of characters. The Preprocessing task involves image acquisition, binarization, noise elimination, skew correction, line and word separation and character segmentation. BanglaOCR allows users to train the data set from any document and observe the recognition performance.Ä«anglaOCR deals will several independent parts as listed below: It takes scanned images of a printed page or document as input and converts them into editable Unicode text. Hidden Markov Model Toolkit (HTK) is used to implement the Training and Classification Task.Ä«anglaOCR is the Optical Character Recognizer for Bangla Script. We used Hidden Markov Model (HMM) technique for pattern training and classification. We performed experiment with several techniques for each individual parts and choose the appropriate methods in our implementation. The entire OCR research and development task is mainly divided into five parts: Preprocessing, Feature Extraction, Training, Recognition and Post-processing.
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This projects aims to develop an Optical Character Recognizer that can recognize Bangla Language Scripts.