This document provides the step-by-step instructions that we followed to train data for Bangla and Devanagari script. This is just a short version of the document TrainingTesseract, which we followed to prepare training data for Bangla and Devanagari. No detail explanation for the purpose of each step is given here.
Data files required
To train Bangla or Devanagari scripts (lang = ban/dev), you have to create 8 data files in the tessdata subdirectory. The 8 files are:
Step by step procedure
Step – 1: Create training data
Preparing training data depends on the characters or units that you want to recognize. Decision about the number of training data units depends on the performance of the segmentation algorithm. If you consider minimal segmentation then you have to consider all the combinations formed by the alphabets of your script. However if your segmentation algorithm is well enough to segment the basic units properly then you can train only the basic and compound units of your script. In the fundamental level we consider training only the basic units. An example of the training data units is shown in figure-1 (Bangla training data) and figure-2 (Devanagari training data).
Figure 1: Training data units for Bangla script
Figure 2: Training data units for Devanagari script
Step – 2: Make Box file
In this step we have to prepare a box file (a text file that lists the characters in the training image, in order, one per line, with the coordinates of the bounding box around the image). To create the box file, run Tesseract on each of your training images using this command line. The command is as follows:
tesseract trainfile.tif trainfile batch.nochop makebox
This will generate a file named trainfile.txt that you have to rename as trainfile.box. Then manually edit the box file where you have to replace the Latin characters (first character of each line) with appropriate unicode Bangla/Devanagari character. If any particular character is broken into two boxes then you have to manually merge the boxes. An example of edited box file is shown in figure-3. The generated box file name must be same with the training tif image file name.
Figure 3: Box file for Bangla and Devanagari script
Step – 3: Run Tesseract for Training
For each of your training image and boxfile pairs, run Tesseract in training mode using the following command:
tesseract trainfile.tif junk nobatch box.train
This will generate a file named trainfile.tr which contains the features of each character of the training page.
Step – 4: Clustering
Clustering is necessary to create prototypes. The character shape features can be clustered using the mftraining and cntraining programs. The mftraining program is invoked using the following command:
This will output two data files: inttemp and pffmtable. (A third file called Microfeat is also written by this program, but it is not used.)
The cntraining program is invoked using the following command:
This will output the normproto data file.
In case of multiple training data the following command will be used:
mftraining trainfile_1.tr trainfile_2.tr ...
cntraining trainfile_1.tr trainfile_2.tr ...
Step – 5: Compute the Character Set
Next you have to generate the unicharset data file using the following command:
This will generate a file named unicharset. Tesseract needs to have access to character properties isalpha, isdigit, isupper, islower. To set these properties we have to manually edit the unicharset file and change the default value (0) set for each training character. An example of the unicharset file is shown in figure-4.
Step – 6: Prepare Dictionary data
Tesseract uses 3 dictionary files for each language. Two of the files are coded as a Directed Acyclic Word Graph (DAWG), and the other is a plain UTF-8 text file. To make the DAWG dictionary files, you first need a wordlist for your language. The wordlist is formatted as a UTF-8 text file with one word per line. Split the wordlist into two sets: the frequent words, and the rest of the words, and then use wordlist2dawg to make the DAWG files:
wordlist2dawg frequent_words_list freq-dawg
wordlist2dawg words_list word-dawg
The third dictionary file is called user-words and is usually empty.
The dictionary files freq-dawg and word-dawg don't have to be given many words if you don't have a wordlist to hand, but accuracy will be lower.
Step – 7: Prepare DangAmbigs file
This file represents the intrinsic ambiguity between characters or sets of characters. You have to generate this file considering the recognition failure example in your script. An example of the rules is shown in figure-5 for Bangla script.
Figure 5: Ambiguity between characters in Bangla script
Step – 8: Rename the necessary files
As mentioned in the starting of this document, now you have to rename the necessary 8 files according to your language/script. For Bangla we used lang=“ban” and for Devanagari we used lang=“dev”. So, the name of the necessary 8 files will be prefixed by lang+'.' (Example: ban.unicharset, dev.unicharset). These 8 files must be copied into the tessdata subdirectory if these are generated any other place.