Name:: BanglaOCR
Summary::
This projects aims to develop an Optical Character Recognizer that can recognize Bangla Language Scripts. The entire OCR research and development task is mainly divided into five parts: Preprocessing, Feature Extraction, Training, Recognition and Post-processing. We performed experiment with several techniques for each individual parts and choose the appropriate methods in our implementation. We used Hidden Markov Model (HMM) technique for pattern training and classification. Hidden Markov Model Toolkit (HTK) is used to implement the Training and Classification Task.
Details::
BanglaOCR is the Optical Character Recognizer for Bangla Script. It takes scanned images of a printed page or document as input and converts them into editable Unicode text. BanglaOCR allows users to train the data set from any document and observe the recognition performance.
BanglaOCR deals will several independent parts as listed below:
- Preprocessing
- Feature Extraction
- Training
- Recognition
- Post-processing
The Preprocessing task involves image acquisition, binarization, noise elimination, skew correction, line and word separation and character segmentation. In this step we put our effort up to minimal segmentation of characters.
The Feature Extraction task involves the extraction of meaningful characteristics of a minimally segmented character. First we divide the character image into several frames using a certain frame length. Then we performed Discrete Cosine Transform (DCT) calculation over each frames. We consider the number of frames and DCT calculated values of each frame as the features for each character.
Training is performed over the calculated features of each minimally segmented character image. We created separate HMM model for each segmented character image. The model creating involves dynamically choosing a prototype HMM model and creates a model using the prototype HMM and the extracted feature data. The model creation task is automatically performed by invoking HInit tool of the HTK toolkit.
The Recognition process invokes the recognition tool HVite of the HTK toolkit. 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 .mmf file where the description of each HMM model is written. All these files are constructed according to HTK Toolkit understandable format. 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.
The final task that BanglaOCR perform is post-processing. This involves a suggestion based spell checker that is capable to identify the erroneous words and produce suggestions. This take the recognizer’s output file as input and produce output file that marks the erroneous words and provides up to a certain number of suggestions.
The project goal of BanglaOCR is to develop a market place standard multilingual OCR system that will be capable to perform the digitization of a wide domain of Bangla Document images. This will help to archive the documents from all spheres and prevent the damage and lost of valuable documents and books.
Team::
Status::
- First version of open source BanglaOCR is released under GNU Public License (GPL) version 2 or later.
- Research work on different parts of BanglaOCR is continuing to enhance the performance and usability.
Research Scope::
- Research on Feature Extraction of Bengali Characters.
- Research on proper Segmentation of Bengali Characters from any type of document image.
- Research on the preprocessing of historical Bangla Document Image.
- Research on Bangla Handwritten Image.
- Research on the Training and Recognition using different techniques.
- Research on Multi Lingual OCR.
Development Scope::
- Implement the existing developed versions using different language.
Timeline:: 2007 – 2009.
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